Camera-LiDAR Sensor Alignment for Multi-Sensor Vehicle Navigation
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Solution Overview
Problem
The challenge lies in effectively combining data from camera and LiDAR sensors for vehicle navigation due to the unknown relative spatial relationship between these sensors, which hinders the integration of their complementary 2D and 3D environmental data.
Innovation Solution
A method is developed to estimate the spatial relationship between a camera and a LiDAR sensor by determining their respective trajectories using sensor data, allowing for the combination of data from both sensors to enhance navigation. This involves capturing environments with both sensors, determining their trajectories, estimating the spatial relationship, and using it to create a combined multi-sensor representation for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If camera and LiDAR sensors are used to capture environment data for navigation, then the quantity and quality of spatial information is improved, but the difficulty of integrating the data increases due to unknown spatial relationship between sensors
Solution Approach 1:
The patent uses an intermediary optimization process that computes the spatial relationship between sensors by matching trajectory points from both sensors. This intermediary computation (solving for transformation parameters) bridges the gap between the two sensor coordinate systems, enabling data integration without requiring pre-calibrated spatial relationships.
Solution Approach 2:
The patent replaces the traditional mechanical calibration approach (physical measurement of sensor positions) with a computational approach. Instead of using mechanical tools to measure and establish spatial relationships, the system uses algorithmic optimization to compute the transformation between sensor coordinate systems based on trajectory data.
2Measurement precision
If the spatial relationship between sensors is determined through trajectory optimization, then the accuracy of sensor data integration is improved, but the computational complexity and time required increases
Solution Approach 1:
The patent performs preliminary actions by collecting trajectory data from both sensors during normal operation before the actual integration is needed. The system accumulates position and orientation data over time, which can then be used for optimization without requiring real-time computation during critical navigation decisions.
Solution Approach 2:
The patent uses more trajectory points than strictly necessary for a basic calibration (excessive action) to improve the accuracy of the spatial relationship estimation. By utilizing multiple corresponding points from both sensors rather than minimal pairs, the optimization achieves higher precision at the cost of increased computation.
3Reliability
If trajectory data from both sensors is used to estimate spatial relationship, then the reliability of sensor integration is improved, but the difficulty of detecting and measuring corresponding points increases
Solution Approach 1:
The patent uses visual features (analogous to color changes) as identifiable markers in the environment that both sensors can detect. By relying on distinctive visual characteristics of environment features, the system makes it easier to identify corresponding points between camera images and LiDAR point clouds.
Data Source
AI summary
This disclosure relates to a method executed by a processor for navigating a moving vehicle through an environment. The processor captures the environment with a first sensor mounted on the moving vehicle to create first sensor data with a second sensor mounted on the moving vehicle to create second sensor data. The processor then determines a first trajectory of the first sensor and a second trajectory of the second sensor. The processor then estimates the spatial relationship between the first sensor and the second sensor based on the first trajectory and the second trajectory and uses the estimated spatial relationship to combine the first sensor data and the second sensor data into a combined multi-sensor representation of the environment. Finally, the processor navigates the moving vehicle based on the combined multi-sensor representation of the environment.