LiDAR Radar Calibration via Planar Target Pose Matrix
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Solution Overview
Problem
LiDAR and radar systems, when used together for object tracking in vehicles, face measurement differences due to separation distance, leading to inaccuracies in dynamic information and imaging, necessitating calibration to enable cooperative object tracking.
Innovation Solution
A method and apparatus for calibrating a LiDAR system at one location with a radar system at another location using a calibration target with a planar reflective area and corner reflectors, determining coefficients and coordinates in respective frames of reference, and composing a cost function to estimate a relative pose matrix that transforms the radar system's frame to the LiDAR system's frame, thereby calibrating their measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LiDAR and radar systems are used together for object tracking, then imaging capability and dynamic information are improved, but measurement precision deteriorates due to separation distance between systems
Solution Approach 1:
A calibration target with known geometry (planar reflective area and corner reflectors) is introduced as an intermediary object to establish a transformation relationship between the LiDAR and radar coordinate systems. The target serves as a common reference that both systems can detect, enabling precise registration despite their physical separation.
Solution Approach 2:
The calibration process creates a virtual copy of the physical calibration target in both LiDAR and radar coordinate systems. By matching the known geometric properties of the target in both systems, a transformation matrix is derived that allows accurate mapping between the two coordinate frames, effectively copying the spatial relationship.
2Manufacturing precision
If LiDAR system is used for imaging surfaces, then imaging quality is improved, but dynamic information reliability deteriorates
Solution Approach 1:
The system merges the strengths of both LiDAR and radar by using LiDAR for high-quality surface imaging and radar for reliable dynamic information extraction. The calibration process enables these two data streams to be integrated into a unified coordinate system, allowing the system to simultaneously achieve high imaging quality and reliable dynamic information.
3Reliability
If radar system is used for providing range and velocity information, then dynamic information is improved, but imaging capability deteriorates
Solution Approach 1:
The system combines radar's superior dynamic information capabilities with LiDAR's imaging strengths. Through calibration, radar-derived velocity and range data are accurately mapped into the LiDAR coordinate system, creating a fused representation that includes both high-quality imagery and reliable dynamic information.
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 calibration allows for accurate transformation of measurements between LiDAR and radar systems, enabling cooperative object tracking and enhanced vehicle navigation by providing reliable range and velocity information along with imaging capabilities.
Implementation Method 1
A LiDAR reflection signal is received from the planar reflective area of the calibration target
Implementation Method 2
A radar reflection signal is received from the plurality of corner reflectors
Data Source
AI summary
A method and apparatus for calibrating a LiDAR system at a first location with a radar system at a second location. A calibration target is placed at a location and orientation with respect to the LiDAR system and the radar system. Coefficients of a plane of the calibration target are determined in a frame of reference of the LiDAR system. Coordinates of the calibration target are determined in a frame of reference of the radar system. A cost function is composed from a planar equation that includes the determined coefficients and the determined coordinates and a relative pose matrix that transforms the frame of reference of the radar system to the frame of reference of the LiDAR system. The cost function is reduced to estimate the relative pose matrix for calibration of the LiDAR system with the radar system.