# Robust Localization, Mapping, and Navigation for Quadruped Robots

arXiv: https://arxiv.org/abs/2505.02272 · HTML: https://mlsanity.com/paper/2505.02272/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 4 May 2025.

## Builds on (most cited first, 21 of 21)

- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Rapid Locomotion via Reinforcement Learning](https://mlsanity.com/paper/2205.02824/) (2022)
- [Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior](https://mlsanity.com/paper/2212.03238/) (2022)
- [A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers](https://mlsanity.com/paper/2207.00254/) (2022)
- [RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation](https://mlsanity.com/paper/2403.06341/) (2024)
- [Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey](https://mlsanity.com/paper/2011.13112/) (2020)
- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [Learning a State Representation and Navigation in Cluttered and Dynamic Environments](https://mlsanity.com/paper/2103.04351/) (2021)
- [VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots](https://mlsanity.com/paper/2107.07243/) (2021)
- [Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion](https://mlsanity.com/paper/2310.17634/) (2023)
- [Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End](https://mlsanity.com/paper/2310.03581/) (2023)
- [Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion](https://mlsanity.com/paper/2010.09259/) (2020)
- [Learning Agile Locomotion on Risky Terrains](https://mlsanity.com/paper/2311.10484/) (2023)
- [Cerberus: Low-Drift Visual-Inertial-Leg Odometry For Agile Locomotion](https://mlsanity.com/paper/2209.07654/) (2022)
- [Mine Tunnel Exploration using Multiple Quadrupedal Robots](https://mlsanity.com/paper/1909.09662/) (2019)
- [ViTAL: Vision-Based Terrain-Aware Locomotion for Legged Robots](https://mlsanity.com/paper/2212.01246/) (2022)
- [RealAnt: An Open-Source Low-Cost Quadruped for Education and Research in Real-World Reinforcement Learning](https://mlsanity.com/paper/2011.03085/) (2020)
- [STEP: State Estimator for Legged Robots Using a Preintegrated foot Velocity Factor](https://mlsanity.com/paper/2202.05572/) (2022)
- [Fast LiDAR Informed Visual Search in Unseen Indoor Environments](https://mlsanity.com/paper/2309.14150/) (2023)

## Built on by (most cited first, 2 of 2)

- [Bridge the Gap: Enhancing Quadruped Locomotion with Vertical Ground Perturbations](https://mlsanity.com/paper/2510.13488/) (2025)
- [MulDP: Multimodal Diffusion Policy for Autonomous Quadruped Parkour Navigation across Complex Terrains](https://mlsanity.com/paper/2609.03984/) (2026)

## Abstract (arXiv)

Quadruped robots are currently a widespread platform for robotics research, thanks to powerful Reinforcement Learning controllers and the availability of cheap and robust commercial platforms. However, to broaden the adoption of the technology in the real world, we require robust navigation stacks relying only on low-cost sensors such as depth cameras. This paper presents a first step towards a robust localization, mapping, and navigation system for low-cost quadruped robots. In pursuit of this objective we combine contact-aided kinematic, visual-inertial odometry, and depth-stabilized vision, enhancing stability and accuracy of the system. Our results in simulation and two different real-world quadruped platforms show that our system can generate an accurate 2D map of the environment, robustly localize itself, and navigate autonomously. Furthermore, we present in-depth ablation studies of the important components of the system and their impact on localization accuracy. Videos, code, and additional experiments can be found on the project website: https://sites.google.com/view/low-cost-quadruped-slam