# Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry

arXiv: https://arxiv.org/abs/1803.02380 · HTML: https://mlsanity.com/paper/1803.02380/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [StablePose: Learning 6D Object Poses from Geometrically Stable Patches](https://mlsanity.com/paper/2102.09334/) (2021)
- [PARF: Primitive-Aware Radiance Fusion for Indoor Scene Novel View Synthesis](https://mlsanity.com/paper/2309.17190/) (2023)
- [EVOPS Benchmark: Evaluation of Plane Segmentation from RGBD and LiDAR Data](https://mlsanity.com/paper/2204.05799/) (2022)
- [TRADE: Object Tracking with 3D Trajectory and Ground Depth Estimates for UAVs](https://mlsanity.com/paper/2210.03270/) (2022)
- [Multi-Resolution Planar Region Extraction for Uneven Terrains](https://mlsanity.com/paper/2311.12562/) (2023)
- [Real-Time Polygonal Semantic Mapping for Humanoid Robot Stair Climbing](https://mlsanity.com/paper/2411.01919/) (2024)

## Abstract (arXiv)

This paper presents CAPE, a method to extract planes and cylinder segments from organized point clouds, which processes 640x480 depth images on a single CPU core at an average of 300 Hz, by operating on a grid of planar cells. While, compared to state-of-the-art plane extraction, the latency of CAPE is more consistent and 4-10 times faster, depending on the scene, we also demonstrate empirically that applying CAPE to visual odometry can improve trajectory estimation on scenes made of cylindrical surfaces (e.g. tunnels), whereas using a plane extraction approach that is not curve-aware deteriorates performance on these scenes. To use these geometric primitives in visual odometry, we propose extending a probabilistic RGB-D odometry framework based on points, lines and planes to cylinder primitives. Following this framework, CAPE runs on fused depth maps and the parameters of cylinders are modelled probabilistically to account for uncertainty and weight accordingly the pose optimization residuals.