Dual-LiDAR Denoising for False Returns in Fog and Debris
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Solution Overview
Problem
Lidar systems often produce false positive detections due to reflections from fine particulate matter such as fog, smoke, and debris, which can obscure true detections and lead to inaccurate vehicle navigation.
Innovation Solution
A method involving two lidar sensors is used to identify and suppress false returns by clustering lidar points, applying metric-based and machine-learned approaches to distinguish between solid objects and particulate matter, and associating confidence scores with lidar data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lidar systems detect reflections from fine particulate matter, then the system can identify potential obstacles, but false positive detections occur that obscure true detections and reduce accuracy
Solution Approach 1:
The patent segments the detection task by using multiple lidar sensors to independently measure distances to the same scene. Each sensor produces separate distance measurements that are then compared and correlated to distinguish true object detections from false positives caused by particulate matter. This segmentation of the detection process across multiple sensors enables the system to identify and suppress false returns while maintaining reliable detection of actual obstacles.
2Measurement precision
If multiple lidar sensors are used to improve detection accuracy, then false returns can be suppressed, but device complexity increases
Solution Approach 1:
The patent merges the output data from multiple lidar sensors through a correlation process that combines distance measurements from different sensors observing the same scene. By correlating the distance data and identifying consistent patterns across sensors, the system achieves improved measurement precision while managing complexity through data fusion rather than processing each sensor independently. This merging approach suppresses false returns that don't correlate across sensors while maintaining detection accuracy.
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 method improves the accuracy of lidar detections by reducing false returns and enhancing the safety and efficiency of vehicle operations in adverse weather conditions.
Implementation Method 1
When the emitted light is incident on a surface, a portion of the light is reflected and received by the light sensor
Implementation Method 2
the system may measure the propagation time of a light signal as it travels from the laser emitter, to the surface, and back to the light sensor
Implementation Method 3
fine particulate matter may also reflect light. Problematically, fog, smoke, fog, exhaust, steam, and other such vapors may reflect light emitted by a lidar system
Data Source
AI summary
Particulate matter, such as fog, snow, rain, steam, vehicle exhaust, debris (plastic bags), etc. may cause one or more sensor types to generate false positive solid surface detections. In particular, various depth measurements may be impeded by particulate matter. Identifying false positive return(s) may comprise clustering lidar points, determining differences in range indicated by two different lidar devices having lidar points in the cluster, determining first differences that are more negative than a negative difference threshold and second differences that are more positive than a positive difference threshold, determining a first portion of lidar data in the cluster associated with the first differences and the second differences is associated with particulate matter or debris, and controlling a vehicle based at least in part on suppressing the first portion of the lidar data or indicating that the first portion of the lidar data is associated with particulate matter or debris.