# Legged Robot State Estimation using Invariant Kalman Filtering and Learned Contact Events

arXiv: https://arxiv.org/abs/2106.15713 · HTML: https://mlsanity.com/paper/2106.15713/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2021.

## Built on by (most cited first, 13 of 13)

- [Robust and Versatile Bipedal Jumping Control through Reinforcement Learning](https://mlsanity.com/paper/2302.09450/) (2023)
- [VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots](https://mlsanity.com/paper/2107.07243/) (2021)
- [Morphological Symmetries in Robotics](https://mlsanity.com/paper/2402.15552/) (2024)
- [On discrete symmetries of robotics systems: A group-theoretic and data-driven analysis](https://mlsanity.com/paper/2302.10433/) (2023)
- [Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras](https://mlsanity.com/paper/2310.04521/) (2023)
- [Energy-based Legged Robots Terrain Traversability Modeling via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2207.03034/) (2022)
- [Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning](https://mlsanity.com/paper/2412.01297/) (2024)
- [Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network](https://mlsanity.com/paper/2402.00366/) (2024)
- [MARG: MAstering Risky Gap Terrains for Legged Robots with Elevation Mapping](https://mlsanity.com/paper/2509.20036/) (2025)
- [Robust Contact State Estimation in Humanoid Walking Gaits](https://mlsanity.com/paper/2208.00278/) (2022)
- [Attention-Based Neural-Augmented Kalman Filter for Legged Robot State Estimation](https://mlsanity.com/paper/2601.18569/) (2026)
- [CoCo-InEKF: State Estimation with Learned Contact Covariances in Dynamic, Contact-Rich Scenarios](https://mlsanity.com/paper/2605.15122/) (2026)
- [TRACE: Learned Proprioceptive Odometry for Legged Robots under Unreliable Contact Conditions](https://mlsanity.com/paper/2608.05975/) (2026)

## Abstract (arXiv)

This work develops a learning-based contact estimator for legged robots that bypasses the need for physical sensors and takes multi-modal proprioceptive sensory data as input. Unlike vision-based state estimators, proprioceptive state estimators are agnostic to perceptually degraded situations such as dark or foggy scenes. While some robots are equipped with dedicated physical sensors to detect necessary contact data for state estimation, some robots do not have dedicated contact sensors, and the addition of such sensors is non-trivial without redesigning the hardware. The trained network can estimate contact events on different terrains. The experiments show that a contact-aided invariant extended Kalman filter can generate accurate odometry trajectories compared to a state-of-the-art visual SLAM system, enabling robust proprioceptive odometry.