Point Cloud Free Space Estimation Under Sensor Noise
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Solution Overview
Problem
Existing systems for autonomous vehicles struggle to efficiently estimate free space by accurately determining the probability of obstructions using sensor data, considering sensor noise, availability, and obstacle heights, which is crucial for safe navigation.
Innovation Solution
A system and method for assigning free space probabilities using point cloud data, involving segmentation, plane detection, grid creation, and probability computation, taking into account sensor noise and obstacle heights, to determine the likelihood of obstacles in the vehicle's path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If free space estimation is performed using sensor data with complex processing (segmentation, plane detection, probability computation), then measurement precision is improved, but device complexity increases
Solution Approach 1:
The point cloud data is segmented into multiple regions or zones based on distance from the sensor. Each segment is processed independently to determine occupancy probability, allowing complex processing to be divided into manageable parts while maintaining overall accuracy
Solution Approach 2:
The problem is transformed from direct obstacle detection to probability estimation in a grid-based spatial framework. By introducing a probability dimension and using plane detection in 3D space, the system achieves more robust free space estimation that accounts for uncertainty and sensor limitations
2Reliability
If sensor noise and availability are taken into account in probability computation, then reliability is improved, but computing time increases
Solution Approach 1:
Sensor characteristics including noise levels and availability are pre-characterized and integrated into the probability computation model beforehand. This allows the system to account for reliability factors without performing complex real-time adjustments during obstacle detection
Solution Approach 2:
The system uses sensor availability and noise characteristics as feedback parameters to continuously adjust occupancy probability estimates. This feedback mechanism improves reliability by adapting to actual sensor performance while maintaining efficient computation through probabilistic modeling
Data Source
AI summary
A system and method for estimating free space and assigning free space probabilities in point cloud data associated with an autonomous vehicle traveling on a surface, including taking into account sensor noise, sensor availability, obstacle heights, and distance of obstacles from the sensor. System and method can include determining surface planes and classifying point cloud points according to whether or not the points fall on surface planes, among other factors.