OHM: GPU Based Occupancy Map Generation

Occupancy grid maps (OGMs) are fundamental to most systems for autonomous robotic navigation. However, CPU-based implementations struggle to keep up with data rates from modern 3D lidar sensors, and provide little capacity for modern extensions which maintain richer voxel representations. This artic...

Full description

Saved in:
Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 7; no. 4; pp. 11078 - 11085
Main Authors: Stepanas, Kazys, Williams, Jason, Hernandez, Emili, Ruetz, Fabio, Hines, Thomas
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Autonomous navigation
Autonomous vehicle navigation
Decay rate
Graphics processing units
Laser radar
Navigation
Occupancy
Performance evaluation
Platforms
Research projects
Robot sensing systems
Robots
sensor fusion
Sensors
software tools for robot programming
Three-dimensional displays
Unmanned ground vehicles
ISSN:2377-3766, 2377-3766
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Occupancy grid maps (OGMs) are fundamental to most systems for autonomous robotic navigation. However, CPU-based implementations struggle to keep up with data rates from modern 3D lidar sensors, and provide little capacity for modern extensions which maintain richer voxel representations. This article presents Occupancy Homogenous Mapping (OHM), our open source, GPU-based OGM framework. We show how the algorithms can be mapped to GPU resources, resolving difficulties with contention to obtain a successful implementation. The implementation supports many modern OGM algorithms including Normal Distributions Transform-Occupancy Maps (NDT-OM), Normal Distributions Transform-Traversability Maps (NDT-TM), decay-rate and Truncated Sign Distance Function (TSDF). A thorough performance evaluation is presented based on tracked and quadruped Uncrewed Ground Vehicle (UGV) platforms and UAVs, and data sets from both outdoor and subterranean environments. The results demonstrate excellent performance improvements both offline, and for online processing in embedded platforms. Finally, we describe how OHM was a key enabler for the UGV navigation solution for our entry in the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge, which placed second at the Final Event.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3196145