Quad-tree Occupancy Grid for 3D Sensor Mapping
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Solution Overview
Problem
Existing radar system-based environment mapping methods face challenges in efficiently updating the negative inverse sensor model (ISM) of an occupancy grid, which is crucial for accurately representing the environment and navigating vehicles without collisions.
Innovation Solution
The method employs a quad-tree structure to assign and update probability values for the negative ISM, allowing for efficient partitioning of solid angle bins and combination with positive ISM values to create a dynamic occupancy grid, facilitating quick updates and accurate mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to update the negative inverse sensor model (ISM) for occupancy grid, then the occupancy grid can be updated, but the updating process is slow and computationally inefficient
Solution Approach 1:
The patent divides the solid angle space into discrete bins organized in a quad-tree structure, segmenting the continuous space into manageable discrete regions. This segmentation allows the system to process and update only relevant portions of the occupancy grid rather than computing entire volumes, significantly improving update efficiency and reducing computational time for the negative ISM.
2Measurement precision
If high accuracy environment mapping is achieved using dual inverse sensor model, then the mapping accuracy improves, but the computational complexity and processing time increases
Solution Approach 1:
The patent pre-computes and stores radial and angular probability components separately before needing to create the final occupancy grid. By preparing these components in advance and organizing them in a quad-tree structure, the system reduces real-time computational complexity while maintaining the accuracy benefits of the dual ISM approach for environment mapping.
3Reliability
If the occupancy grid is updated frequently to reflect current environment state, then the accuracy of collision avoidance improves, but the processing load and energy consumption increases
Solution Approach 1:
The patent applies different processing levels to different regions of the occupancy grid based on their importance and detection status. The quad-tree structure allows fine-grained control where only specific solid angle bins requiring updates are processed, rather than uniformly updating the entire grid. This local quality approach reduces overall energy consumption while maintaining reliability for collision-critical regions.
Data Source
AI summary
A vehicle, system and method of mapping the environment is disclosed. The system includes a sensor and a processor. The sensor is configured to obtain a detection from an object in an environment surrounding the vehicle. The processor is configured to compute a plurality of radial components and a plurality of angular components for a positive inverse sensor model (ISM) of an occupancy grid, select a radial component corresponding to a range of the detection from the plurality of radial components and selecting an angular component corresponding to an angle of the detection from the plurality of angular components, multiply the selected radial component and the selected angular component to create an occupancy grid for the detection, and map the environment using the occupancy grid.