LIDAR Extrinsic Matrix Accuracy Detector
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Solution Overview
Problem
Inaccurate extrinsic matrices used in LIDAR fusion processes in autonomous driving vehicles can lead to errors in object detection and perception, due to faulty calibration or pose movement of LIDAR devices, which are not effectively addressed by existing calibration methods.
Innovation Solution
A computer-implemented method to determine the accuracy of extrinsic matrices by analyzing the distribution of coordinate values from post-LIDAR fusion point clouds, prompting recalibration when multiple peaks are detected, ensuring accurate transformation of point clouds into a uniform frame of reference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing calibration methods are used, then the calibration process is simple, but the accuracy of extrinsic matrices deteriorates due to faulty calibration or pose movement
Solution Approach 1:
The patent implements a feedback mechanism by continuously monitoring the distribution of coordinate values from LIDAR fusion point clouds and comparing them against expected patterns. When deviations indicate inaccurate extrinsic matrices, the system automatically triggers recalibration procedures, creating a closed-loop feedback system that maintains calibration accuracy without requiring manual intervention
Solution Approach 2:
The patent replaces manual calibration procedures with an automated computational approach. Instead of relying on physical calibration objects or manual adjustment mechanisms, the system uses algorithmic analysis of point cloud coordinate distributions to detect and correct extrinsic matrix inaccuracies, substituting mechanical calibration processes with digital signal processing and statistical analysis
2Area of stationary object
If multiple LIDAR devices are used to reduce blind areas, then coverage improves, but the complexity of managing and calibrating multiple extrinsic matrices increases
Solution Approach 1:
The patent merges the calibration management of multiple LIDAR devices into a unified framework. By analyzing the coordinate value distributions from all LIDAR devices simultaneously and using a common reference frame, the system consolidates what would otherwise be separate calibration management tasks into a single coordinated process, reducing overall complexity while maintaining comprehensive coverage
3Reliability
If extrinsic matrices are updated frequently to account for pose movement, then accuracy is maintained, but the frequency of recalibration increases system complexity
Solution Approach 1:
The patent performs preliminary analysis of coordinate value distributions continuously in the background, preparing detection signals that indicate when recalibration is needed. This preliminary monitoring action allows the system to maintain reliability by detecting pose movements early, while avoiding unnecessary recalibration operations that would reduce productivity, thereby optimizing the balance between reliability and efficiency
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method ensures accurate extrinsic matrix calibration, reducing errors in object detection and perception processes by identifying and correcting inaccuracies in LIDAR fusion, thereby enhancing the reliability of autonomous driving systems.
Implementation Method 1
Individual points in the point cloud can be determined by transmitting a laser pulse and detecting a returning pulse, if any, reflected from the object, and determining the distance to the object according to the time delay between the transmitted pulse and the reception of the reflected pulse
Implementation Method 2
A LIDAR device can estimate a distance to an object while scanning through a scene to assemble a point cloud representing a reflective surface of the object
Data Source
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Figure 2
Figure 3A
AI summary
A method, apparatus, and system for determining whether all extrinsic matrices are accurate is disclosed. A plurality of post-LIDAR fusion point clouds that are based on simultaneous outputs from a plurality of LIDAR devices installed at one ADV are obtained (610). The obtained plurality of point clouds are filtered to obtain a first set of points comprising all points in the plurality of point clouds that fall within a region of interest (620). Each point in the first set of points corresponds to one coordinate value on the axis in the up-down direction (630). A distribution of the first plurality of coordinate values is obtained (640). A quantity of peaks in the distribution of the first plurality of coordinate values is determined (650). Whether all extrinsic matrices associated with the plurality of LIDAR devices are accurate is determined based on the quantity of peaks in the distribution of the first plurality of coordinate values (660).