OHM: GPU Based Occupancy Map Generation

TL;DR

OHM is a GPU-accelerated framework for occupancy grid map generation that significantly improves performance for autonomous navigation systems using modern 3D lidar sensors.

Contribution

This paper introduces OHM, an open source GPU-based framework supporting advanced OGM algorithms, enabling real-time processing for autonomous robotic platforms.

Findings

Achieves high performance in online and offline OGM processing

Supports multiple modern OGM algorithms including NDT-OM and TSDF

Enabled UGV navigation in DARPA Subterranean Challenge

Abstract

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 paper presents 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 NDT-OM, NDT-TM, decay-rate and TSDF. A thorough performance evaluation is presented based on tracked and quadruped 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 DARPA Subterranean Challenge, which placed second at the Final Event.