Doppler LiDAR Point-Cloud Processing for INS-Failure Localization
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Solution Overview
Problem
Autonomous navigation systems, particularly in vehicles, face reliability issues due to the unreliable performance of inertial navigation solutions (INS), which can be improved by integrating high-resolution range-Doppler LIDAR data for enhanced localization and obstacle detection.
Innovation Solution
Implementing a method on a processor to operate a Doppler LIDAR system that collects point cloud data with inclination, azimuth, range, and relative speed dimensions, allowing for the determination of object properties and vehicle position, velocity, and global velocity by isolating high-value Doppler components and stationary points, and using this data to correct for ego-motion and detect moving objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR system collects comprehensive point cloud data with multiple dimensions, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system extracts only the essential features from comprehensive LIDAR point cloud data - specifically range and velocity information derived from Doppler shifts. By focusing on these key parameters and discarding redundant data, the system achieves high measurement precision while keeping processing complexity manageable.
Solution Approach 2:
The patent replaces complex mechanical processing with optical Doppler effect-based velocity measurement. Instead of using multiple sensors or complex mechanisms to measure velocity, the system uses the Doppler shift of the LIDAR return signal, which provides velocity information directly through optical frequency changes, simplifying the overall system while maintaining precision.
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 approach enhances the reliability of autonomous navigation by improving vehicle localization and obstacle detection, even when INS components fail, by providing accurate position and velocity data and enabling better collision avoidance and route management.
Implementation Method 1
a laser source to produce a transmitted signal, a scanner to scan the transmitted signal across a field of view to illuminate spots on external objects, and a detector array to detect light reflected from the spots
Implementation Method 2
direct ranging based on round trip travel time of an optical pulse to an object
Implementation Method 3
operating a Doppler LIDAR system to collect point cloud data that indicates for each point at least four dimensions including an inclination angle, an azimuthal angle, a range, and relative speed between the point and the LIDAR system
Data Source
AI summary
Techniques for controlling an autonomous vehicle with a processor that controls operation, includes operating a Doppler LIDAR system to collect point cloud data that indicates for each point at least four dimensions including an inclination angle, an azimuthal angle, a range, and relative speed between the point and the LIDAR system. A value of a property of an object in the point cloud is determined based on only three or fewer of the at least four dimensions. In some of embodiments, determining the value of the property of the object includes isolating multiple points in the point cloud data which have high value Doppler components. A moving object within the plurality of points is determined based on a cluster by azimuth and Doppler component values.