Automated Lidar Calibration via Odometry Misalignment Detection
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Solution Overview
Problem
Existing lidar systems require manual calibration, which is inefficient and disrupts vehicle operation, and there is a need for automated calibration that can be performed during normal vehicle operation without significant impact.
Innovation Solution
An automated lidar system calibration method that uses lidar odometry data to detect misalignments and recalibrate the system by determining yaw and pitch mounting offsets, allowing for continuous calibration during vehicle operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration is performed, then calibration accuracy is achieved, but vehicle operation is disrupted and calibration efficiency is low
Solution Approach 1:
The lidar system performs self-calibration by automatically detecting misalignments between the lidar coordinate system and vehicle coordinate system using captured point cloud data, eliminating the need for manual calibration operations while maintaining calibration accuracy
Solution Approach 2:
The system dynamically adjusts calibration parameters by computing transformation matrices and correction values based on real-time odometry data and point cloud analysis, enabling continuous optimization of calibration accuracy without manual intervention
2Measurement precision
If manual calibration is performed, then calibration accuracy is achieved, but vehicle operation time is lost
Solution Approach 1:
The calibration process continues uninterrupted during normal vehicle operation by utilizing real-time odometry data and point cloud captures, allowing the system to perform calibration tasks continuously without stopping or pausing vehicle operations
Solution Approach 2:
The system performs calibration computations and adjustments in real-time during vehicle operation, proactively maintaining accurate alignment between coordinate systems before misalignments affect performance
3Productivity
If automated calibration is implemented, then calibration efficiency is improved, but system complexity increases
Solution Approach 1:
The calibration system leverages existing lidar components and odometry data processing capabilities to perform multiple functions including misalignment detection, transformation matrix computation, and real-time calibration adjustments, avoiding the need for dedicated specialized hardware
Solution Approach 2:
The system replaces manual mechanical calibration procedures with automated computational methods using point cloud data analysis and coordinate transformation algorithms, eliminating the need for physical adjustment mechanisms while improving 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
The method enables efficient and continuous calibration of lidar systems with minimal disruption to vehicle operation, improving installation and monitoring accuracy over time, and ensuring optimal performance.
Implementation Method 1
The system determines the distance to the target based on one or more characteristics associated with the received light. For example, the lidar system may determine the distance to the target based on the time of flight for a pulse of light emitted by the light source to travel to the target and back to the lidar system.
Data Source
AI summary
A direction of motion associated with a lidar device is detected to fall within a threshold. In response to the detection that the direction of the motion is within the threshold, a directional vector associated with an orientation of the lidar device is determined. Based on a difference between the direction of the motion and the directional vector, one or more correction values for the lidar device is determined.