# Real-time Spatial-temporal Traversability Assessment via Feature-based Sparse Gaussian Process

arXiv: https://arxiv.org/abs/2503.04134 · HTML: https://mlsanity.com/paper/2503.04134/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Elevation Mapping for Locomotion and Navigation using GPU](https://mlsanity.com/paper/2204.12876/) (2022)
- [ScaTE: A Scalable Framework for Self-Supervised Traversability Estimation in Unstructured Environments](https://mlsanity.com/paper/2209.06522/) (2022)
- [Geometrically Constrained Trajectory Optimization for Multicopters](https://mlsanity.com/paper/2103.00190/) (2021)
- [STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation; Results from the DARPA Subterranean Challenge](https://mlsanity.com/paper/2303.01614/) (2023)
- [Traversability Analysis for Autonomous Driving in Complex Environment: A LiDAR-based Terrain Modeling Approach](https://mlsanity.com/paper/2307.02060/) (2023)
- [PUTN: A Plane-fitting based Uneven Terrain Navigation Framework](https://mlsanity.com/paper/2203.04541/) (2022)
- [Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains](https://mlsanity.com/paper/2303.01169/) (2023)
- [An Efficient Trajectory Planner for Car-like Robots on Uneven Terrain](https://mlsanity.com/paper/2309.06115/) (2023)
- [Autonomous Mapless Navigation on Uneven Terrains](https://mlsanity.com/paper/2402.13443/) (2024)
- [Gaussian Process-based Traversability Analysis for Terrain Mapless Navigation](https://mlsanity.com/paper/2403.19010/) (2024)
- [Universal Trajectory Optimization Framework for Differential Drive Robot Class](https://mlsanity.com/paper/2409.07924/) (2024)
- [Elevation State-Space: Surfel-Based Navigation in Uneven Environments for Mobile Robots](https://mlsanity.com/paper/2208.08202/) (2022)

## Abstract (arXiv)

Terrain analysis is critical for the practical ap- plication of ground mobile robots in real-world tasks, espe- cially in outdoor unstructured environments. In this paper, we propose a novel spatial-temporal traversability assessment method, which aims to enable autonomous robots to effectively navigate through complex terrains. Our approach utilizes sparse Gaussian processes (SGP) to extract geometric features (curvature, gradient, elevation, etc.) directly from point cloud scans. These features are then used to construct a high- resolution local traversability map. Then, we design a spatial- temporal Bayesian Gaussian kernel (BGK) inference method to dynamically evaluate traversability scores, integrating historical and real-time data while considering factors such as slope, flatness, gradient, and uncertainty metrics. GPU acceleration is applied in the feature extraction step, and the system achieves real-time performance. Extensive simulation experiments across diverse terrain scenarios demonstrate that our method outper- forms SOTA approaches in both accuracy and computational efficiency. Additionally, we develop an autonomous navigation framework integrated with the traversability map and validate it with a differential driven vehicle in complex outdoor envi- ronments. Our code will be open-source for further research and development by the community, https://github.com/ZJU-FAST-Lab/FSGP_BGK.