# DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments

arXiv: https://arxiv.org/abs/2102.05117 · HTML: https://mlsanity.com/paper/2102.05117/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 77 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration](https://mlsanity.com/paper/1807.09413/) (2018)
- [SegMatch: Segment based loop-closure for 3D point clouds](https://mlsanity.com/paper/1609.07720/) (2016)
- [DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams](https://mlsanity.com/paper/1909.12198/) (2019)
- [SegMap: 3D Segment Mapping using Data-Driven Descriptors](https://mlsanity.com/paper/1804.09557/) (2018)
- [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)
- [LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time](https://mlsanity.com/paper/2012.14447/) (2020)
- [Distributed Mapping with Privacy and Communication Constraints: Lightweight Algorithms and Object-based Models](https://mlsanity.com/paper/1702.03435/) (2017)
- [Towards Resilient Autonomous Navigation of Drones](https://mlsanity.com/paper/2008.09679/) (2020)
- [Confidence-rich grid mapping](https://mlsanity.com/paper/2006.15754/) (2020)
- [LION: Lidar-Inertial Observability-Aware Navigator for Vision-Denied Environments](https://mlsanity.com/paper/2102.03443/) (2021)

## Built on by (most cited first, 9 of 9)

- [NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge](https://mlsanity.com/paper/2103.11470/) (2021)
- [A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments](https://mlsanity.com/paper/2203.06612/) (2022)
- [UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT](https://mlsanity.com/paper/2206.08185/) (2022)
- [Event Camera and LiDAR based Human Tracking for Adverse Lighting Conditions in Subterranean Environments](https://mlsanity.com/paper/2304.08908/) (2023)
- [Real-Time Simultaneous Localization and Mapping with LiDAR intensity](https://mlsanity.com/paper/2301.09257/) (2023)
- [Neural Error Covariance Estimation for Precise LiDAR Localization](https://mlsanity.com/paper/2501.02558/) (2025)
- [CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments](https://mlsanity.com/paper/2505.06483/) (2025)
- [Towards Robust LiDAR Localization: Deep Learning-based Uncertainty Estimation](https://mlsanity.com/paper/2509.18954/) (2025)
- [Bag-of-Word-Groups (BoWG): A Robust and Efficient Loop Closure Detection Method Under Perceptual Aliasing](https://mlsanity.com/paper/2510.22529/) (2025)

## Abstract (arXiv)

Enabling fully autonomous robots capable of navigating and exploring large-scale, unknown and complex environments has been at the core of robotics research for several decades. A key requirement in autonomous exploration is building accurate and consistent maps of the unknown environment that can be used for reliable navigation. Loop closure detection, the ability to assert that a robot has returned to a previously visited location, is crucial for consistent mapping as it reduces the drift caused by error accumulation in the estimated robot trajectory. Moreover, in multi-robot systems, loop closures enable merging local maps obtained by a team of robots into a consistent global map of the environment. In this paper, we present a degeneracy-aware and drift-resilient loop closing method to improve place recognition and resolve 3D location ambiguities for simultaneous localization and mapping (SLAM) in GPS-denied, large-scale and perceptually-degraded environments. More specifically, we focus on SLAM in subterranean environments (e.g., lava tubes, caves, and mines) that represent examples of complex and ambiguous environments where current methods have inadequate performance.