Volumetric Environment Mapping for Robot Path Planning
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Solution Overview
Problem
Existing autonomous and semi-autonomous machines face challenges in cost-effective environment reconstruction and path planning due to the high cost of 3D LiDAR sensors and the computational expense of vision-based perception systems, limiting their ability to accurately detect and navigate around obstacles in dynamic environments.
Innovation Solution
The use of parallelized computations on GPUs and PPUs to compute Euclidean Signed Distance Field (ESDF)-based cost maps for path planning, integrating Truncated Signed Distance Field (TSDF) and visualization meshes, which allows for efficient obstacle detection and path planning while reducing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D LiDAR sensors are used for accurate obstacle detection and path planning, then measurement precision and reliability are improved, but device cost increases significantly
Solution Approach 1:
The patent creates a volumetric copy of the environment using depth images and point clouds to generate a 3D mesh representation. This digital twin or virtual model serves as a substitute for direct LiDAR measurements, enabling accurate path planning and obstacle detection through computational geometry rather than expensive hardware sensors.
Solution Approach 2:
The patent replaces the mechanical LiDAR sensing system with a vision-based system using cameras and depth sensors. By substituting optical/mechanical measurement devices with computational image processing and photogrammetry techniques, the system achieves comparable measurement precision while dramatically reducing hardware costs.
2Ease of manufacture
If vision-based perception systems are used for environment reconstruction, then device cost is reduced, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the environment reconstruction process into distinct computational stages: depth map generation from images, point cloud creation from depth maps, 3D mesh construction from point clouds, and volumetric map generation from meshes. This segmentation allows each stage to be optimized independently and processed efficiently, reducing overall computational complexity.
Solution Approach 2:
The patent performs preliminary processing of images to generate depth maps and point clouds before constructing the final 3D mesh. By pre-computing intermediate representations and organizing data in advance, the system reduces the computational burden during real-time path planning and environment reconstruction.
3Manufacturing precision
If surface reconstruction methods are used for environment mapping, then manufacturing precision is improved, but adaptability for path planning is reduced
Solution Approach 1:
The patent transitions from 2D surface reconstruction to 3D volumetric mapping by extruding mesh surfaces into volumetric cells and creating signed distance fields. This dimensional expansion transforms the representation from surface-only geometry to full volumetric space, enabling path planning algorithms to reason about free space, obstacles, and navigation paths in three dimensions.
Solution Approach 2:
The patent changes the fundamental parameter representation from surface coordinates to volumetric distance fields. By computing signed distance values for each voxel in the 3D grid, the system transforms geometric surface data into a parameterized volumetric representation that directly encodes navigation information, making the map adaptable for path planning while preserving surface reconstruction accuracy.
Data Source
AI summary
Approaches for environment reconstruction and path planning for autonomous machine systems and applications are described. An iterative volumetric mapping function for an ego-machine may compute a distance field, and from the distance field derive a cost map representing a volumetric reconstruction of the physical environment around the ego-machine. The cost map may be used for collision avoidance and path planning. The iterative volumetric mapping function may also optionally compute a color integration map and visualization mesh from the distance field that can be used for visualization of the physical environment around the ego-machine. The cost map may be computed as a Euclidean Signed Distance Field (ESDF) and the distance field from which the cost map is computed may include a Truncated Signed Distance Field (TSDF). The distance field, cost map, color integration map and visualization mesh may each be stored in memory as maps of a plurality of map layers.