Year: 2016
Type: conference-paper
Abstract: In mobile robotics applications, generation of accurate static maps is encumbered by the presence of ephemeral objects such as vehicles, pedestrians, or bicycles. We propose a method to process a sequence of laser point clouds and back-fill dense surfaces into gaps caused by removing objects from the scene - a valuable tool in scenarios where resource constraints permit only one mapping pass in a particular region. Our method processes laser scans in a three-dimensional voxel grid using the Truncated Signed Distance Function (TSDF) and then uses a Total Variation (TV) regulariser with a Kernel Conditional Density Estimation (KCDE) “soft” data term to interpolate missing surfaces. Using four scenarios captured with a push-broom 2D laser, our technique infills approximately 20 m2 of missing surface area for each removed object. Our reconstruction's median error ranges between 5.64 cm - 9.24 cm with standard deviations between 4.57 cm - 6.08 cm.
Authors: Michael Tanner, Pedro Pinies, Lina Maria Paz, Paul Newman
Institution: University of Oxford
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2419502228