# OHM: GPU Based Occupancy Map Generation

arXiv: https://arxiv.org/abs/2206.06079 · HTML: https://mlsanity.com/paper/2206.06079/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [Heterogeneous Ground and Air Platforms, Homogeneous Sensing: Team CSIRO Data61's Approach to the DARPA Subterranean Challenge](https://mlsanity.com/paper/2104.09053/) (2021)
- [Virtual Surfaces and Attitude Aware Planning and Behaviours for Negative Obstacle Navigation](https://mlsanity.com/paper/2010.16018/) (2020)
- [An Analytical Lidar Sensor Model Based on Ray Path Information](https://mlsanity.com/paper/1910.10469/) (2019)
- [What is the Best Grid-Map for Self-Driving Cars Localization? An Evaluation under Diverse Types of Illumination, Traffic, and Environment](https://mlsanity.com/paper/2009.09308/) (2020)

## Abstract (arXiv)

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.