Introduction
Autonomous systems depend on continuous and reliable sensor data to understand their surroundings and monitor their own condition. Cameras, LiDAR, radar, GPS receivers, ultrasonic sensors, inertial measurement units, wheel encoders, microphones, and environmental sensors generate large quantities of information during operation.
However, raw sensor data cannot usually be used directly for perception, navigation, planning, or control. Sensor measurements may contain noise, distortion, missing values, repeated observations, incorrect readings, timing differences, and differences in scale or format. Before artificial-intelligence models and autonomous algorithms can interpret the information, the data must be collected, organized, synchronized, cleaned, transformed, and validated.
Sensor data collection refers to the process of receiving and recording measurements from sensing devices. Preprocessing refers to the preparation of this raw information so that it becomes accurate, consistent, efficient, and suitable for further analysis.
The complete process can be represented as:
Sensing → Data Acquisition → Timestamping → Calibration → Synchronization → Cleaning → Transformation → Validation → Storage or Real-Time Processing
The quality of preprocessing directly affects the reliability of the autonomous system. Even an advanced machine-learning model may produce incorrect results when the input data is incomplete, poorly calibrated, delayed, or inconsistent.
Sensor Data Collection
Sensor data collection begins when sensing devices observe the environment or measure the condition of the autonomous platform.
A camera collects images or video frames. LiDAR produces three-dimensional point clouds. Radar generates range, angle, and velocity measurements. GPS provides geographic coordinates, speed, and timing information. Ultrasonic sensors measure nearby distance, while inertial sensors record acceleration and rotation.
Each sensor collects data at a particular frequency. A camera may record thirty or sixty frames per second. LiDAR may update ten or twenty times per second. Radar may produce measurements at another frequency, while an inertial measurement unit may generate hundreds or thousands of readings per second.
The system must receive these different data streams without losing important information. This requires suitable data-acquisition hardware, communication interfaces, processing units, and software.
Data may be collected through wired connections, wireless communication, automotive networks, robotic middleware, serial interfaces, industrial protocols, or internal buses.
The collection process must preserve the original measurement, the time at which it was recorded, the sensor that produced it, and any information about sensor configuration.
If the system cannot determine when or where a measurement was captured, the data may not be useful for navigation or sensor fusion.
Real-Time and Offline Data Collection
Sensor data can be collected for real-time operation or for later analysis.
In real-time collection, the data is processed immediately so that the autonomous system can respond to current conditions. A vehicle must analyse camera and radar data quickly enough to identify a pedestrian and apply braking. A drone must process inertial measurements continuously to maintain stable flight.
Real-time systems must meet strict timing requirements. Delayed data may no longer represent the current environment.
Offline collection stores the data for later use. Engineers may collect driving videos, LiDAR scans, robot trajectories, machine vibrations, or drone-flight measurements to develop and test algorithms.
Offline datasets are used for machine-learning training, system evaluation, fault analysis, simulation, and research.
The same autonomous system may perform both functions. It processes data in real time for immediate control while storing selected measurements for future analysis.
Data Sampling
Sampling refers to the frequency at which a sensor records measurements.
The sampling rate must be high enough to capture important environmental changes.
A slowly changing temperature sensor may require only a few measurements each second. A fast-moving drone may require hundreds of inertial measurements per second to maintain stability.
If the sampling rate is too low, rapid changes may be missed. This can create inaccurate movement estimates or delayed responses.
For example, a vehicle travelling at high speed covers a significant distance within one second. A low camera frame rate may fail to capture a brief hazard or fast-moving object.
A higher sampling rate provides more detail but also generates more data. This increases storage, communication, and computing requirements.
Engineers must therefore select a sampling rate that balances accuracy, speed, power consumption, bandwidth, and processing capacity.
Timestamping
Every sensor measurement should include an accurate timestamp.
A timestamp records the time at which the data was captured. It allows measurements from different sensors to be compared and combined.
Timestamping is important because autonomous systems use several sensors that operate independently and at different frequencies.
Suppose a camera identifies a vehicle while radar estimates its speed. If the image and radar measurement were captured at different moments, the detected positions may not match.
The error becomes greater when the autonomous platform or nearby objects are moving quickly.
Accurate timestamps allow the system to align observations and determine which measurements represent the same environmental state.
Timestamps may be generated by the sensor, the processing unit, or a central clock. A shared time reference improves consistency across the system.
Clock drift can occur when different devices gradually develop small timing differences. Autonomous platforms therefore use time-synchronization methods to keep sensor clocks aligned.
Sensor Synchronization
Synchronization is the process of aligning data from different sensors according to time.
Some sensors provide measurements at the same frequency, while others operate at very different rates.
An inertial measurement unit may generate two hundred readings during the time required for a camera to capture one frame. LiDAR may produce another measurement between camera frames.
The system must determine which inertial, LiDAR, radar, and GPS measurements correspond to each camera image.
Exact synchronization captures several sensor measurements at nearly the same time. Approximate synchronization matches measurements that occur within an acceptable time interval.
Interpolation may be used when a required measurement is not available at the exact time. The system estimates a value between two nearby observations.
For example, if two GPS positions are available before and after a camera frame, the system may estimate the vehicle’s location at the camera timestamp.
Poor synchronization can create inaccurate sensor fusion, object location, mapping, and movement estimation.
Sensor Calibration
Calibration ensures that sensor measurements correspond accurately to the physical world.
A sensor may contain small manufacturing differences, installation errors, or measurement bias. Calibration identifies and corrects these differences.
Internal calibration determines the properties of an individual sensor.
For a camera, internal calibration includes focal length, optical centre, pixel scale, and lens distortion.
For an inertial sensor, calibration may identify measurement bias, scale error, and axis misalignment.
For a LiDAR system, calibration may correct distance or angular measurement errors.
External calibration determines the position and orientation of one sensor relative to another sensor or to the autonomous platform.
A camera, LiDAR, radar, and GPS antenna may be installed at different locations on a vehicle. Each produces measurements in its own coordinate system.
External calibration allows the system to transform these measurements into a shared coordinate frame.
Without accurate calibration, a camera may detect an object in one location while LiDAR places the same object somewhere else. This misalignment can produce unsafe planning decisions.
Calibration should be performed during installation and checked after maintenance, physical impact, sensor replacement, or long periods of operation.
Coordinate Systems and Transformations
Autonomous systems often use several coordinate systems.
A camera describes objects in image coordinates. LiDAR provides points relative to the LiDAR sensor. GPS provides geographic coordinates. A robot may use a local map coordinate system, while the controller may use a body-centred coordinate frame.
To combine the data, the system must convert measurements from one coordinate system into another.
A coordinate transformation describes how position and orientation change between frames.
For example, a pedestrian detected in a camera image must be connected to the corresponding LiDAR points. The camera and LiDAR measurements must first be transformed into the same reference frame.
Robot Operating System environments commonly use transformation frameworks to manage relationships among sensor, robot, map, and world coordinates.
Incorrect transformations can cause significant errors in localization, mapping, object detection, and movement control.
Types of Raw Sensor Data
Camera data normally consists of images or video frames. Each frame contains pixel values representing brightness, colour, or infrared intensity.
LiDAR data consists of point clouds. Each point may include three-dimensional position, reflection intensity, and timestamp information.
Radar data may contain range, angle, relative velocity, and signal strength.
GPS data includes latitude, longitude, altitude, speed, heading, and timing information.
Ultrasonic sensors usually provide a distance value and sometimes a signal-quality measurement.
Inertial measurement units produce acceleration and angular-velocity readings along several axes.
Wheel encoders measure wheel rotation, which can be converted into travelled distance and speed.
Because these formats are different, preprocessing methods must be selected according to the sensor type.
Data Cleaning
Data cleaning removes or corrects inaccurate, incomplete, repeated, or unreliable measurements.
Sensors may generate faulty readings because of environmental interference, electrical noise, blocked views, communication problems, or hardware limitations.
A camera frame may be blurred. A LiDAR scan may contain isolated points caused by rain. GPS may suddenly report an incorrect position. An ultrasonic sensor may fail to detect a soft or angled surface.
The preprocessing system must identify these problems before the data is used.
Cleaning may involve removing invalid values, correcting known errors, filtering noisy measurements, replacing missing values, or marking uncertain observations.
The system should not remove unusual data automatically without evaluation. An unusual measurement may represent a real hazard rather than a sensor error.
For example, an unexpected object in a road scene should not be discarded simply because it differs from previous observations.
Noise Reduction
Noise is unwanted variation in sensor measurements.
Electronic components, vibration, weather, lighting, reflections, temperature, and communication interference can introduce noise.
Image noise may appear as random changes in pixel brightness or colour. LiDAR noise may appear as isolated or inaccurate points. GPS noise may cause gradual or sudden location variation. Inertial sensors may produce small continuous errors.
Filtering reduces noise while attempting to preserve important information.
A moving-average filter replaces a measurement with the average of nearby observations. This smooths rapid fluctuations but may also delay the response.
A median filter is useful for removing isolated extreme values. In image processing, it can reduce impulse noise while preserving edges.
Gaussian filtering smooths image data by averaging neighbouring pixels according to a weighted distribution.
Kalman filtering combines current measurements with previous estimates to calculate a more reliable state. It is widely used in tracking, navigation, and sensor fusion.
The chosen filter must match the sensor and application. Excessive filtering may remove important details or create delays.
Handling Missing Data
Sensor data may be missing because of communication loss, sensor blockage, hardware failure, poor visibility, or temporary environmental conditions.
A camera frame may be dropped. GPS may become unavailable in a tunnel. Radar may fail to return a clear measurement. A LiDAR sensor may produce incomplete data because of heavy rain.
The system must identify missing values and decide how to respond.
Short gaps may be estimated using previous and future measurements. This process is known as interpolation.
The system may also use measurements from another sensor. If GPS becomes unavailable, the vehicle may continue estimating movement using an inertial sensor, wheel encoder, camera, and map.
Long or critical data loss should not be hidden through estimation. The system may need to reduce speed, restrict autonomous functions, stop safely, or request human assistance.
The response depends on the importance of the missing sensor and the availability of reliable alternatives.
Removing Outliers
An outlier is a measurement that differs greatly from expected observations.
A GPS receiver may suddenly place a robot several metres away from its actual path. A LiDAR point may appear far outside the normal scene. An inertial sensor may produce a sudden unrealistic acceleration value.
Outliers may result from noise, reflection, communication error, sensor malfunction, or temporary interference.
The system can identify outliers by comparing measurements with physical limits, historical values, neighbouring data, or other sensors.
If a robot is known to move slowly, a position change of fifty metres within one second is likely incorrect.
Statistical methods may also identify values far outside the normal distribution.
Outlier removal must be applied carefully. Sudden changes may represent real events. An emergency stop produces rapid deceleration and should not be removed as an error.
Image Preprocessing
Camera images require several preprocessing steps before they can be used for computer vision or deep learning.
Image resizing changes the image dimensions. Smaller images reduce processing requirements but may remove fine details.
Colour conversion changes the image representation. An RGB image may be converted to grayscale for edge detection or to another colour space for object separation.
Normalization adjusts pixel values to a consistent range. This supports stable machine-learning model performance.
Noise reduction improves image quality, while contrast enhancement makes important features easier to detect.
Lens-distortion correction removes curved or stretched appearance caused by camera optics.
Image stabilization reduces unwanted movement caused by vibration or platform motion.
Cropping removes irrelevant image regions and focuses processing on an area of interest.
For example, a lane-detection system may process only the lower part of a road image.
Brightness and exposure correction can reduce the effect of changing lighting, although extreme darkness or glare may still create difficulties.
LiDAR Data Preprocessing
LiDAR produces large three-dimensional point clouds that require significant processing.
The first step may involve removing invalid or low-confidence points.
Ground filtering separates the road or floor surface from raised objects. This helps identify vehicles, people, trees, shelves, and obstacles.
Downsampling reduces the number of points while preserving important geometric information. This lowers memory and computation requirements.
Voxel-grid filtering divides space into small three-dimensional cells and represents points within each cell using a smaller set of values.
Clustering groups points that likely belong to the same object.
Coordinate transformation places the point cloud in the robot, vehicle, or map frame.
Motion correction may be required because the sensor and objects can move while the scan is being captured. Without correction, the point cloud may appear distorted.
LiDAR intensity values may also be normalized or filtered to improve interpretation.
Radar Data Preprocessing
Radar measurements may contain reflections from vehicles, buildings, road surfaces, signs, vegetation, and weather.
The preprocessing system must distinguish meaningful targets from unwanted signals.
Noise suppression reduces low-quality reflections.
Thresholding removes signals below a selected confidence or strength level.
Clutter removal reduces repeated reflections from stationary background objects.
Doppler processing estimates the relative speed of detected targets.
Radar detections may be grouped into clusters representing possible objects.
Tracking algorithms connect radar observations over time.
Radar data may also require calibration and coordinate transformation before it can be combined with camera or LiDAR measurements.
Because radar has lower spatial detail than cameras, preprocessing and sensor fusion are important for correct object interpretation.
GPS Data Preprocessing
GPS measurements may contain noise, gradual drift, signal loss, or sudden position errors.
The system may smooth the coordinates using filtering methods. It may also remove impossible position changes.
Map matching connects the estimated GPS position to a likely road, path, or known route.
Correction services can improve GPS accuracy. Differential methods compare measurements with a known reference station, while Real-Time Kinematic systems provide higher precision under suitable conditions.
GPS data is commonly fused with inertial measurements and wheel odometry.
When satellite signals are weak, the system relies more heavily on other sensors. When GPS becomes reliable again, it corrects accumulated movement error.
Preprocessing should also detect possible interference, jamming, or spoofed signals.
Ultrasonic Data Preprocessing
Ultrasonic sensors provide simple distance measurements but can be affected by surface angle, material, temperature, and nearby sensors.
A single unusual reading may be removed using a median filter or comparison with previous measurements.
Repeated observations can be averaged to reduce small fluctuations.
Measurements outside the valid operating range should be marked as invalid rather than treated as real distance values.
When several ultrasonic sensors operate close together, sound pulses may interfere. The system may activate sensors at different times to reduce cross-interference.
Because ultrasonic sensors provide limited environmental detail, their readings are often combined with cameras, LiDAR, or other sensors.
Inertial Sensor Preprocessing
Inertial measurement units normally contain accelerometers and gyroscopes.
Accelerometers measure linear acceleration, while gyroscopes measure rotational movement.
These sensors produce high-frequency data but may contain bias, drift, vibration, and temperature-related error.
Bias correction removes a constant or slowly changing measurement offset.
Low-pass filtering reduces high-frequency vibration.
Sensor orientation must be calibrated so that acceleration and rotation correspond correctly to the platform axes.
Gravity compensation separates the effect of gravity from actual platform acceleration.
Gyroscope drift accumulates over time and may cause increasing orientation error. The system therefore combines inertial data with GPS, cameras, LiDAR, magnetometers, or wheel encoders.
Data Normalization
Different sensor values may have different units and numerical ranges.
A camera pixel may range from zero to 255. GPS coordinates use geographic values. Speed may be measured in metres per second, while temperature may be measured in degrees.
Machine-learning and data-analysis systems often perform better when values are converted into comparable numerical ranges.
Normalization may scale values between zero and one. Standardization may transform data so that it has a defined mean and variation.
The method should preserve the physical meaning of the data.
For control and safety applications, the system must also retain original units or provide clear conversion.
Incorrect scaling can produce unreliable predictions and control commands.
Data Transformation and Feature Extraction
Raw sensor data may contain more detail than the algorithm requires.
Feature extraction converts raw information into useful characteristics.
In images, features may include edges, corners, textures, object regions, or learned neural-network representations.
In LiDAR data, features may include surfaces, clusters, object dimensions, or geometric landmarks.
In radar data, features may include range, speed, direction, and signal strength.
In inertial data, features may include average acceleration, vibration frequency, orientation change, or movement pattern.
Feature extraction reduces data size and focuses the analysis on meaningful information.
Traditional algorithms use manually designed features, while deep-learning models may learn features automatically from data.
Data Compression
High-resolution sensors generate large quantities of data.
Compression reduces file size and communication requirements.
Images and videos may be compressed using standard formats. Point clouds may be downsampled or stored using efficient structures. Repeated measurements may be summarized.
Lossless compression reduces size without removing information. Lossy compression removes selected details to achieve greater reduction.
Lossy compression must be used carefully in safety-critical systems. Important small objects, edges, or distance details may be lost.
Real-time systems must also consider the computing time required for compression and decompression.
Data Validation
Validation checks whether the processed data is suitable for use.
The system may verify that values remain within physical limits, timestamps are correct, sensor frames are available, and calibration parameters are valid.
Cross-sensor validation compares information from different sources.
If a camera detects a nearby vehicle but LiDAR and radar provide no supporting measurements, the system may treat the detection with lower confidence.
If GPS indicates movement while wheel encoders and inertial sensors show that the robot is stationary, the system may identify a possible GPS error.
Validation improves reliability by preventing one incorrect sensor reading from controlling the complete autonomous system.
Data Quality
Sensor-data quality can be evaluated through accuracy, precision, completeness, consistency, timeliness, and reliability.
Accuracy describes how close a measurement is to the true value.
Precision describes whether repeated measurements produce similar results.
Completeness describes whether all required data is available.
Consistency means that values from different sensors or time periods do not conflict without explanation.
Timeliness means that the data is recent enough to support the current decision.
Reliability describes whether the sensor performs consistently under expected conditions.
A dataset can be precise but inaccurate. A GPS receiver may repeatedly report nearly the same location while that location remains several metres away from the true position.
Autonomous systems must therefore evaluate several aspects of data quality rather than relying on a single measure.
Data Labelling for Machine Learning
Machine-learning systems often require labelled sensor data.
Labels describe what the data represents.
An image may contain labels for pedestrians, vehicles, signs, lanes, and road areas. A LiDAR point cloud may identify ground, buildings, vehicles, and vegetation.
Radar data may include target type and movement. Robot sensor data may be labelled as normal operation, collision, wheel slip, or equipment fault.
Data labelling can be performed manually, automatically, or through a combination of methods.
Manual labelling can provide high quality but is slow and expensive. Automatic labelling is faster but may introduce errors.
Labels must be reviewed because incorrect annotations can train a model to produce incorrect results.
For autonomous systems, rare and safety-critical situations must be represented carefully.
Dataset Balance and Diversity
A sensor dataset should represent the range of conditions expected during operation.
A vehicle dataset should include daytime, nighttime, rain, fog, urban roads, highways, pedestrians, cyclists, emergency vehicles, construction zones, and unusual objects.
A drone dataset may require different altitudes, seasons, weather conditions, terrains, and camera angles.
A warehouse robot dataset should include different lighting, layouts, workers, packages, obstacles, and floor conditions.
If the dataset contains mainly normal and easy situations, the model may perform poorly during rare or difficult events.
Class imbalance occurs when some categories appear much more often than others.
A model may learn to identify common vehicles but perform poorly for motorcycles, wheelchairs, animals, or uncommon obstacles.
Preprocessing and dataset design may include sampling, data augmentation, or targeted data collection to improve balance.
Data Augmentation
Data augmentation creates modified versions of existing data to increase variety.
Images may be rotated, cropped, resized, brightened, darkened, blurred, or affected by simulated rain or fog.
Point clouds may be rotated, translated, reduced, or altered with controlled noise.
Sensor measurements may be transformed to represent different operating conditions.
Augmentation helps machine-learning models generalize to new situations.
However, the transformed data must remain physically realistic. An unrealistic image or sensor pattern may teach the model incorrect relationships.
Augmentation should support real expected conditions rather than create arbitrary changes.
Real-Time Preprocessing Pipeline
In real-time autonomous operation, preprocessing must occur within a limited time.
The pipeline receives sensor data, verifies timestamps, corrects calibration, removes obvious noise, transforms coordinate systems, and prepares the input for perception and localization.
The processing time must be shorter than the interval between required decisions.
If a vehicle camera captures images every thirty-three milliseconds, the system must process the data quickly enough to avoid an increasing delay.
Some preprocessing tasks are performed on specialized processors, graphics-processing units, or embedded AI hardware.
Different tasks may run at different frequencies. Motor-control data may be processed hundreds of times per second, while map updates may occur less frequently.
The system must prioritize safety-critical information such as obstacle detection, braking, and platform stability.
Data Storage
Sensor data may be stored for system development, monitoring, investigation, and improvement.
Stored information may include raw measurements, processed data, system decisions, control commands, warnings, and detected faults.
Data storage supports debugging and failure analysis. Engineers can review what the system sensed and why it selected a particular action.
Stored data is also used to retrain machine-learning models and evaluate performance.
However, cameras, microphones, and location sensors may collect sensitive personal information.
Organizations must consider privacy, data protection, access control, retention periods, and secure storage.
Not every measurement needs to be stored permanently. Systems may save only selected events, compressed data, or short periods before and after an important incident.
Data Communication
Sensor information must move between sensors, processors, controllers, storage devices, and external platforms.
Communication systems must provide sufficient bandwidth and low delay.
A high-resolution camera or LiDAR can generate large data streams. A slow connection may cause data loss or delay.
Critical communication may require reliable protocols, error detection, message priority, and backup connections.
Wireless communication is useful for fleet management and remote monitoring, but local autonomous decisions should normally remain possible when the network is unavailable.
A communication failure should not prevent the system from entering a safe condition.
Edge and Cloud Processing
Edge processing occurs directly on or near the autonomous platform.
It reduces delay and allows operation without continuous internet access.
Obstacle detection, stabilization, localization, planning, and emergency response are commonly processed at the edge.
Cloud processing provides larger computing and storage resources.
It may be used for model training, long-term analysis, fleet coordination, map updates, and data backup.
An autonomous system may send selected processed information to the cloud while keeping immediate safety decisions local.
The division between edge and cloud depends on latency, privacy, bandwidth, energy, reliability, and application requirements.
Monitoring Sensor Health
The system must continuously monitor whether sensors are operating correctly.
Sensor-health monitoring may detect blocked cameras, dirty lenses, excessive vibration, overheating, missing data, communication errors, inaccurate timestamps, or unusual measurements.
A camera image that remains unchanged for an extended period may indicate a frozen data stream. A sudden increase in LiDAR noise may indicate weather interference or physical obstruction.
The autonomous system should estimate the reliability of each sensor and adjust its behaviour.
If one sensor becomes unreliable, the system may use alternative sensors, reduce speed, limit autonomy, stop safely, or request maintenance.
Example of Preprocessing in an Autonomous Car
An autonomous car receives camera images, LiDAR scans, radar detections, GPS coordinates, wheel measurements, and inertial data.
The system timestamps each measurement and synchronizes the streams.
Camera images are corrected for lens distortion, resized, normalized, and prepared for object-detection models.
LiDAR scans are filtered, downsampled, corrected for vehicle movement, and transformed into the vehicle coordinate frame.
Radar signals are processed to remove clutter and estimate object speed.
GPS readings are filtered and fused with inertial and wheel measurements.
The processed data is validated and combined to create a consistent environmental model.
The perception, localization, prediction, and planning systems then use this information to control the vehicle.
Example of Preprocessing in a Drone
A drone collects camera frames, GPS position, altitude, acceleration, rotation, and obstacle-distance measurements.
The inertial measurements are filtered to reduce vibration and corrected for sensor bias.
Camera images are stabilized and adjusted for motion. GPS readings are smoothed and combined with inertial data.
Altitude measurements are validated using several sensors.
The synchronized data allows the flight controller to estimate the drone’s current position and orientation.
The perception system processes camera or LiDAR data to detect structures, terrain, and obstacles.
When a sensor becomes unreliable, the drone changes its navigation method or enters a safe fallback mode.
Example of Preprocessing in a Mobile Robot
A warehouse robot collects LiDAR scans, camera images, wheel-encoder values, ultrasonic measurements, and inertial data.
LiDAR points are filtered and transformed into the robot coordinate frame.
Wheel measurements are checked for unrealistic changes and combined with inertial data to estimate movement.
Camera images are resized and normalized for person and package detection.
Ultrasonic values are filtered to remove isolated false echoes.
The sensor streams are synchronized and compared.
If the wheel encoders indicate movement but LiDAR and camera observations remain unchanged, the system may detect wheel slip.
The processed information supports mapping, localization, route planning, and collision avoidance.
Challenges in Sensor Data Preprocessing
Sensor preprocessing must manage large data volume, different formats, timing differences, environmental interference, hardware limitations, and strict real-time requirements.
A method that improves accuracy may also increase processing delay.
Aggressive filtering may remove useful information. Weak filtering may allow noise to affect decisions.
Calibration may gradually change because of vibration, temperature, impact, or mechanical movement.
Machine-learning preprocessing must remain consistent between training and deployment. A model trained using one normalization method may perform poorly if live data is processed differently.
Testing must therefore include normal conditions, difficult environments, sensor failures, communication delays, and unusual events.
Conclusion
Sensor data collection and preprocessing transform raw measurements into reliable information that autonomous systems can use.
Data collection receives measurements from cameras, LiDAR, radar, GPS, ultrasonic sensors, inertial units, wheel encoders, and other devices.
Timestamping and synchronization align information from sensors operating at different frequencies. Calibration and coordinate transformation ensure that measurements correspond correctly to the physical environment and to one another.
Data cleaning removes invalid values, noise, outliers, and repeated or incomplete information. Missing measurements may be estimated or replaced using alternative sensors when safe to do so.
Different sensor types require different preprocessing. Images may be resized, normalized, corrected, and enhanced. LiDAR point clouds may be filtered, downsampled, segmented, and transformed. Radar data may require clutter removal and velocity processing. GPS, ultrasonic, and inertial measurements require filtering, correction, and validation.
Feature extraction, normalization, compression, labelling, augmentation, and data-quality checks prepare information for machine learning and autonomous algorithms.
Real-time preprocessing must provide accurate results within strict timing limits. Edge processing supports immediate decisions, while cloud platforms support storage, model training, and long-term analysis.
Reliable preprocessing does not only improve model accuracy. It supports safer perception, localization, planning, control, and fault management.
An autonomous system can make effective decisions only when its sensor data is timely, calibrated, synchronized, clean, consistent, and representative of the real operating environment.
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