What Lies Behind: Recovering Hidden Shape In Dense Mapping

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 2Dlaser, our technique infills approximately 20 m(2) 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.