# Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information

arXiv: https://arxiv.org/abs/2506.09548 · HTML: https://mlsanity.com/paper/2506.09548/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots](https://mlsanity.com/paper/2107.07243/) (2021)
- [Learning Inertial Odometry for Dynamic Legged Robot State Estimation](https://mlsanity.com/paper/2111.00789/) (2021)
- [Globally Consistent 3D LiDAR Mapping with GPU-accelerated GICP Matching Cost Factors](https://mlsanity.com/paper/2109.07073/) (2021)
- [Hybrid Contact Preintegration for Visual-Inertial-Contact State Estimation Using Factor Graphs](https://mlsanity.com/paper/1803.07531/) (2018)
- [Robust Legged Robot State Estimation Using Factor Graph Optimization](https://mlsanity.com/paper/1904.03048/) (2019)
- [Tightly-Coupled LiDAR-IMU-Wheel Odometry with an Online Neural Kinematic Model Learning via Factor Graph Optimization](https://mlsanity.com/paper/2407.08907/) (2024)

## Built on by (most cited first, 1 of 1)

- [KING: Embodiment-Aware Kinematic Graph Neural Network for Unified Motion Representation of Legged and Wheeled Robots](https://mlsanity.com/paper/2608.01015/) (2026)

## Abstract (arXiv)

In this letter, we present tightly coupled LiDAR-IMU-leg odometry, which is robust to challenging conditions such as featureless environments and deformable terrains. We developed an online learning-based leg kinematics model named the neural leg kinematics model, which incorporates tactile information (foot reaction force) to implicitly express the nonlinear dynamics between robot feet and the ground. Online training of this model enhances its adaptability to weight load changes of a robot (e.g., assuming delivery or transportation tasks) and terrain conditions. According to the textitneural adaptive leg odometry factor and online uncertainty estimation of the leg kinematics model-based motion predictions, we jointly solve online training of this kinematics model and odometry estimation on a unified factor graph to retain the consistency of both. The proposed method was verified through real experiments using a quadruped robot in two challenging situations: 1) a sandy beach, representing an extremely featureless area with a deformable terrain, and 2) a campus, including multiple featureless areas and terrain types of asphalt, gravel (deformable terrain), and grass. Experimental results showed that our odometry estimation incorporating the textitneural leg kinematics model outperforms state-of-the-art works. Our project page is available for further details: https://takuokawara.github.io/RAL2025projectpage/