Doppler LiDAR Point-Cloud Localization Beyond INS Failure
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Solution Overview
Problem
Current autonomous navigation systems, particularly in vehicles, rely on unreliable inertial navigation solutions (INS) and face challenges in accurately localizing vehicles when components fail, especially in detecting dynamic obstacles, which can lead to unreliable position and velocity data.
Innovation Solution
Implementing a high-resolution Doppler LIDAR system that collects point cloud data with inclination, azimuth, range, and relative speed dimensions to determine object properties, allowing for improved localization and obstacle detection by isolating high-value Doppler components and stationary points, and using known object positions to calculate the LIDAR system's position and velocity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inertial navigation system (INS) components are used for vehicle localization, then position and velocity data can be obtained, but the system becomes unreliable when components fail
Solution Approach 1:
The patent introduces LIDAR as an intermediary sensing system to measure ranges to external objects independently of INS. The LIDAR system provides alternative measurement data that can compensate for INS failures, acting as a mediator to maintain navigation reliability when primary INS components become unreliable
Solution Approach 2:
The patent changes the measurement parameters from INS-estimated position/velocity to LIDAR-measured ranges to multiple external objects. By using range measurements to stationary and moving objects, the system can calculate position and velocity through trilateration and Doppler effects, providing an alternative parameter set that maintains navigation functionality
2Measurement precision
If LIDAR systems use short pulse lasers with high peak power to achieve acceptable range accuracy, then range detection sensitivity improves, but optical components degrade rapidly
Solution Approach 1:
The patent changes the LIDAR operating parameters from short high-power pulses to long low-power continuous or extended pulses. By using frequency modulation (chirp) or phase modulation on continuous waves, the system achieves acceptable range accuracy through signal processing techniques while maintaining significantly lower peak power levels that preserve optical component lifespan
Solution Approach 2:
The patent replaces direct time-of-flight measurement with frequency or phase-based measurement techniques. By using heterodyne detection or interferometric methods, the system achieves precise range measurements through optical frequency differences rather than relying on high-power pulse timing, thereby reducing mechanical and optical stress
3Reliability
If multiple sensors (cameras, radars, LIDAR) are combined for spatial awareness, then navigation reliability improves, but system complexity increases
Solution Approach 1:
The patent makes the LIDAR system multi-functional by enabling it to perform both range measurement to stationary objects for localization and range measurement to moving objects for obstacle detection. This universal approach allows a single sensor type to fulfill multiple navigation functions, reducing the need for separate specialized sensors and thereby managing system complexity
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
Enhances the reliability of autonomous vehicle navigation by providing accurate localization and obstacle detection even when INS components fail, improving control and safety by utilizing Doppler LIDAR data to compensate for INS unreliability.
Implementation Method 1
Optical detection of range using lasers, often referenced by a mnemonic, LIDAR, for light detection and ranging
Implementation Method 2
Recent work by current inventors, show a novel arrangement of optical components and coherent processing to detect Doppler shifts in returned signals
Implementation Method 3
using the same modulated optical carrier as a reference signal that is combined with the returned signal at an optical detector to produce in the resulting electrical signal a relatively low beat frequency in the RF band that is proportional to the difference in frequencies or phases
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.