Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry
This addresses visual odometry challenges in environments with cylindrical surfaces, such as tunnels, by providing a faster and more consistent method, though it is incremental as it extends existing probabilistic frameworks.
The paper tackles the problem of extracting planes and cylinders from depth images for visual odometry, presenting CAPE, which processes 640x480 depth images at 300 Hz on a single CPU core and improves trajectory estimation on cylindrical scenes by 4-10 times faster than state-of-the-art methods.
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.