LiDAR Camera Calibration via Semantic Segmentation
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Solution Overview
Problem
Current methods for calibrating LIDAR and camera sensors together in vehicles are inefficient, often requiring specialized devices and resulting in sensor misalignments that degrade data quality and disrupt autonomous navigation.
Innovation Solution
A system and method using semantic segmentation to correlate camera images with point cloud data, allowing for dynamic on-demand calibration of sensor parameters to align sensor data without the need for purpose-built calibration devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods using specialized calibration devices are employed, then calibration accuracy can be achieved, but the process requires service center visits or interrupts vehicle use
Solution Approach 1:
The system performs calibration autonomously using environmental features automatically detected by the sensors themselves. The processor identifies calibration features in the environment and uses them to calculate and apply calibration adjustments without external intervention, enabling the vehicle to self-calibrate during normal operation.
Solution Approach 2:
The system continuously captures and processes sensor data to identify calibration features in the environment before calibration is needed. By maintaining a ready supply of identified calibration features and pre-calculating potential adjustments, the system can perform calibration instantly when required, eliminating service interruptions.
2Measurement precision
If traditional calibration methods are used, then sensor alignment can be corrected, but specialized calibration devices and service center visits are required
Solution Approach 1:
The vehicle uses its own existing sensors (LIDAR and camera) to detect environmental calibration features and perform self-calibration. No external calibration devices are needed - the system leverages naturally occurring environmental features and the vehicle's own sensor suite to maintain alignment accuracy.
Solution Approach 2:
The calibration system uses the same sensors (LIDAR and camera) for both their primary sensing functions and for calibration purposes. The processor serves dual roles in both navigation and calibration calculations, eliminating the need for specialized calibration equipment while maintaining alignment accuracy.
3Measurement precision
If sensors are calibrated together using traditional methods, then data alignment is achieved, but the process is complex and requires specialized instrumentation
Solution Approach 1:
The vehicle's existing processor and sensors are repurposed to perform calibration functions. The same computational hardware that handles navigation data also calculates calibration adjustments, and the same sensors used for sensing are used to detect calibration features, eliminating the need for complex specialized calibration instrumentation.
Solution Approach 2:
The LIDAR and camera sensors serve dual purposes: their primary sensing function for navigation and a secondary calibration function by detecting environmental calibration features. The processor similarly handles both navigation computations and calibration calculations, simplifying the overall system architecture by eliminating dedicated calibration hardware.
4Measurement precision
If LIDAR and camera data are fused together, then perception accuracy is improved, but sensor drift causes misalignments that degrade data quality
Solution Approach 1:
The system continuously monitors sensor data for misalignments by comparing LIDAR and camera observations of the same environmental features. When drift is detected, the processor calculates correction adjustments and applies them to realign the sensor data streams, creating a closed-loop feedback system that maintains data quality over time.
Solution Approach 2:
The system performs frequent calibration updates using environmental features to prevent drift accumulation. By proactively detecting and correcting misalignments before they significantly degrade data quality, the system maintains reliable sensor fusion without requiring extensive recalibration procedures.
Data Source
AI summary
System, methods, and other embodiments described herein relate to calibrating a light detection and ranging (LiDAR) sensor with a camera sensor. In one embodiment, a method includes controlling i) the LiDAR sensor to acquire point cloud data, and ii) the camera sensor to acquire an image. The point cloud data and the image at least partially overlap in relation to a field of view of a surrounding environment. The method includes projecting the point cloud data into the image to form a combined image. The method includes adjusting sensor parameters of the LiDAR sensor and the camera sensor according to the combined image to calibrate the LiDAR sensor and the camera sensor together.