# Multi-Resolution Planar Region Extraction for Uneven Terrains

arXiv: https://arxiv.org/abs/2311.12562 · HTML: https://mlsanity.com/paper/2311.12562/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 21 Nov 2023.

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

- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots](https://mlsanity.com/paper/2306.14874/) (2023)
- [Perceptive Locomotion through Nonlinear Model Predictive Control](https://mlsanity.com/paper/2208.08373/) (2022)
- [Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control](https://mlsanity.com/paper/2011.00032/) (2020)
- [Receding-Horizon Perceptive Trajectory Optimization for Dynamic Legged Locomotion with Learned Initialization](https://mlsanity.com/paper/2104.09078/) (2021)
- [Simultaneous Contact, Gait and Motion Planning for Robust Multi-Legged Locomotion via Mixed-Integer Convex Optimization](https://mlsanity.com/paper/1904.04595/) (2019)
- [Footstep Planning for Autonomous Walking Over Rough Terrain](https://mlsanity.com/paper/1907.08673/) (2019)
- [Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry](https://mlsanity.com/paper/1803.02380/) (2018)
- [Polytopic Planar Region Characterization of Rough Terrains for Legged Locomotion](https://mlsanity.com/paper/2207.03098/) (2022)

## Abstract (arXiv)

This paper studies the problem of extracting planar regions in uneven terrains from unordered point cloud measurements. Such a problem is critical in various robotic applications such as robotic perceptive locomotion. While existing approaches have shown promising results in effectively extracting planar regions from the environment, they often suffer from issues such as low computational efficiency or loss of resolution. To address these issues, we propose a multi-resolution planar region extraction strategy in this paper that balances the accuracy in boundaries and computational efficiency. Our method begins with a pointwise classification preprocessing module, which categorizes all sampled points according to their local geometric properties to facilitate multi-resolution segmentation. Subsequently, we arrange the categorized points using an octree, followed by an in-depth analysis of nodes to finish multi-resolution plane segmentation. The efficiency and robustness of the proposed approach are verified via synthetic and real-world experiments, demonstrating our method's ability to generalize effectively across various uneven terrains while maintaining real-time performance, achieving frame rates exceeding 35 FPS.