GPU-Accelerated Collision Analysis of Vehicles in a Point Cloud Environment

We present a GPU-accelerated collision detection method for the navigation of vehicles in enclosed spaces represented using large point clouds. Our approach takes a CAD model of a vehicle, converts it to a volumetric representation or voxels, and computes the collision of the voxels with a point clo...

Full description

Saved in:
Bibliographic Details
Published in:IEEE computer graphics and applications 2022-09, Vol.42 (5), p.37-50
Main Authors: Shah, Harshil, Ghadai, Sambit, Gamdha, Dhruv, Schuster, Alex, Thomas, Ivan, Greiner, Nathan, Krishnamurthy, Adarsh
Format: Magazinearticle
Language:English
Subjects:
Algorithms
Clearance Analysis
Clearances
Cloud computing
Collision avoidance
Collision Detection
Computational modeling
Data models
Finite element method
Graphics processing units
Mesh generation
Minkowski Sums
Navigation
Point Cloud
Point cloud compression
Solid modeling
Vehicle crash testing
Vehicle routing
Voxelization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present a GPU-accelerated collision detection method for the navigation of vehicles in enclosed spaces represented using large point clouds. Our approach takes a CAD model of a vehicle, converts it to a volumetric representation or voxels, and computes the collision of the voxels with a point cloud representing the environment to identify a suitable path for navigation. We perform adaptive and efficient collision of voxels with the point cloud without the need for mesh generation. We have developed a GPU-accelerated voxel Minkowski sum algorithm to perform a clearance analysis of the vehicle. Finally, we provide theoretical bounds for the accuracy of the collision and clearance analysis. Our GPU implementation is linked with Unreal Engine to provide flexibility in performing the analysis.
ISSN:0272-1716
1558-1756
DOI:10.1109/MCG.2022.3177890