# Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions

arXiv: https://arxiv.org/abs/2101.11110 · HTML: https://mlsanity.com/paper/2101.11110/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion](https://mlsanity.com/paper/2010.09259/) (2020)
- [LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments](https://mlsanity.com/paper/2003.01744/) (2020)
- [Towards Resilient Autonomous Navigation of Drones](https://mlsanity.com/paper/2008.09679/) (2020)
- [Confidence-rich grid mapping](https://mlsanity.com/paper/2006.15754/) (2020)
- [Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems](https://mlsanity.com/paper/1808.00031/) (2018)
- [NanoMap: Fast, Uncertainty-Aware Proximity Queries with Lazy Search over Local 3D Data](https://mlsanity.com/paper/1802.09076/) (2018)
- [Pose Estimation of Vehicles Over Uneven Terrain](https://mlsanity.com/paper/1903.02052/) (2019)

## Built on by (most cited first, 4 of 4)

- [NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge](https://mlsanity.com/paper/2103.11470/) (2021)
- [STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation](https://mlsanity.com/paper/2103.02828/) (2021)
- [Learning Risk-aware Costmaps for Traversability in Challenging Environments](https://mlsanity.com/paper/2107.11722/) (2021)
- [Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data](https://mlsanity.com/paper/2210.14721/) (2022)

## Abstract (arXiv)

We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffs and holes, and narrow passages. Environments are GPS-denied and perceptually-degraded with variable lighting from dark to lit and obscurants (dust, fog, smoke). Lack of prior maps and degraded communication eliminates the possibility of prior or off-board computation or operator intervention. This necessitates real-time on-board computation using noisy sensor data. To address these challenges, we propose a resilient architecture that exploits redundancy and heterogeneity in sensing modalities. Further resilience is achieved by triggering recovery behaviors upon failure. We propose a fast settling algorithm to generate robust multi-fidelity traversability estimates in real-time. The proposed approach was deployed on multiple physical systems including skid-steer and tracked robots, a high-speed RC car and legged robots, as a part of Team CoSTAR's effort to the DARPA Subterranean Challenge, where the team won 2nd and 1st place in the Tunnel and Urban Circuits, respectively.