Lidar Reflection Map Calibration for Autonomous Driving
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Solution Overview
Problem
There is a lack of efficient methods for calibrating LIDAR devices in autonomous vehicles, which is crucial for accurate navigation and obstacle detection, as existing methods are not scalable for mass production.
Innovation Solution
A LIDAR calibration system that uses a coordinate converter, represented by a quaternion function, to translate LIDAR images from a local coordinate system to a global coordinate system, optimizing parameters by comparing dynamic and reference reflection maps to minimize differences and ensure accurate obstacle detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional LIDAR calibration methods are used, then calibration can be performed, but the process is inefficient and not scalable for mass production
Solution Approach 1:
The system performs preliminary actions by pre-collecting LIDAR data from multiple vehicles in various environments to create a reference reflection map before actual calibration occurs. This pre-prepared reference data enables faster, more scalable calibration without requiring complex real-time calibration procedures for each vehicle
Solution Approach 2:
The invention creates a virtual reference reflection map that replicates real-world environmental reflections. This digital copy serves as a standardized reference that can be reused across mass production calibration processes, eliminating the need for physical calibration targets or complex real-world calibration scenarios for each vehicle
2Measurement precision
If LIDAR calibration is performed frequently to maintain accuracy, then detection precision improves, but time consumption increases
Solution Approach 1:
The calibration system performs self-service by automatically comparing the vehicle's LIDAR reflection map against the pre-established reference reflection map and autonomously determining calibration parameters without requiring manual intervention or extensive real-time calibration procedures, thus maintaining high accuracy with minimal time investment
Data Source
AI summary
In one embodiment, a set of LIDAR images are received representing the LIDAR point cloud data captured by a LIDAR device of an autonomous driving vehicle (ADV) at different point in times. Each of the LIDAR imagers is transformed or translated from a local coordinate system (e.g., LIDAR coordinate space) to a global coordinate system (e.g., GPS coordinate space) using a coordinate converter configured with a set of parameters. A first LIDAR reflection map is generated based on the transformed LIDAR images, for example, by merging the transformed LIDAR images together. The coordinate converter is optimized by adjusting one or more parameters of the coordinate converter based on the difference between the first LIDAR reflection map and a second LIDAR reflection map that serves as a reference LIDAR reflection map. The optimized coordinate converter can then be utilized to process LIDAR data for autonomous driving at real-time.