# Slip-Based Autonomous ZUPT through Gaussian Process to Improve Planetary Rover Localization

arXiv: https://arxiv.org/abs/2103.07587 · HTML: https://mlsanity.com/paper/2103.07587/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 13 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Gaussian process regression for forecasting battery state of health](https://mlsanity.com/paper/1703.05687/) (2017)
- [RINS-W: Robust Inertial Navigation System on Wheels](https://mlsanity.com/paper/1903.02210/) (2019)
- [Improved Planetary Rover Inertial Navigation and Wheel Odometry Performance through Periodic Use of Zero-Type Constraints](https://mlsanity.com/paper/1906.08849/) (2019)
- [Perception-aware Autonomous Mast Motion Planning for Planetary Exploration Rovers](https://mlsanity.com/paper/1912.06898/) (2019)

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

- [pyrtklib: An open-source package for tightly coupled deep learning and GNSS integration for positioning in urban canyons](https://mlsanity.com/paper/2409.12996/) (2024)
- [Learning Rock Pushability on Rough Planetary Terrain](https://mlsanity.com/paper/2505.09833/) (2025)

## Abstract (arXiv)

The zero-velocity update (ZUPT) algorithm provides valuable state information to maintain the inertial navigation system (INS) reliability when stationary conditions are satisfied. Employing ZUPT along with leveraging non-holonomic constraints can greatly benefit wheeled mobile robot dead-reckoning localization accuracy. However, determining how often they should be employed requires consideration to balance localization accuracy and traversal rate for planetary rovers. To address this, we investigate when to autonomously initiate stops to improve wheel-inertial odometry (WIO) localization performance with ZUPT. To do this, we propose a 3D dead-reckoning approach that predicts wheel slippage while the rover is in motion and forecasts the appropriate time to stop without changing any rover hardware or major rover operations. We validate with field tests that our approach is viable on different terrain types and achieves a 3D localization accuracy of more than 97% over 650 m drives on rough terrain.