# Cerberus: Low-Drift Visual-Inertial-Leg Odometry For Agile Locomotion

arXiv: https://arxiv.org/abs/2209.07654 · HTML: https://mlsanity.com/paper/2209.07654/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2022.

## Built on by (most cited first, 10 of 10)

- [Robot Parkour Learning](https://mlsanity.com/paper/2309.05665/) (2023)
- [Helpful DoggyBot: Open-World Object Fetching using Legged Robots and Vision-Language Models](https://mlsanity.com/paper/2410.00231/) (2024)
- [Robust Localization, Mapping, and Navigation for Quadruped Robots](https://mlsanity.com/paper/2505.02272/) (2025)
- [Reinforcement Learning with Lie Group Orientations for Robotics](https://mlsanity.com/paper/2409.11935/) (2024)
- [Monocular Person Localization under Camera Ego-motion](https://mlsanity.com/paper/2503.02916/) (2025)
- [Holistic Fusion: Task- and Setup-Agnostic Robot Localization and State Estimation with Factor Graphs](https://mlsanity.com/paper/2504.06479/) (2025)
- [KING: Embodiment-Aware Kinematic Graph Neural Network for Unified Motion Representation of Legged and Wheeled Robots](https://mlsanity.com/paper/2608.01015/) (2026)
- [OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering](https://mlsanity.com/paper/2401.16719/) (2024)
- [A Novel ViDAR Device With Visual Inertial Encoder Odometry and Reinforcement Learning-Based Active SLAM Method](https://mlsanity.com/paper/2506.13100/) (2025)
- [Fauna Sprout: A lightweight, approachable, developer-ready humanoid robot](https://mlsanity.com/paper/2601.18963/) (2026)

## Abstract (arXiv)

We present an open-source Visual-Inertial-Leg Odometry (VILO) state estimation solution, Cerberus, for legged robots that estimates position precisely on various terrains in real time using a set of standard sensors, including stereo cameras, IMU, joint encoders, and contact sensors. In addition to estimating robot states, we also perform online kinematic parameter calibration and contact outlier rejection to substantially reduce position drift. Hardware experiments in various indoor and outdoor environments validate that calibrating kinematic parameters within the Cerberus can reduce estimation drift to lower than 1% during long distance high speed locomotion. Our drift results are better than any other state estimation method using the same set of sensors reported in the literature. Moreover, our state estimator performs well even when the robot is experiencing large impacts and camera occlusion. The implementation of the state estimator, along with the datasets used to compute our results, are available at https://github.com/ShuoYangRobotics/Cerberus.