Coupling of localization and depth data for mapping using Intel RealSense T265 and D435i cameras
This work addresses mapping for robotics or indoor navigation, but it is incremental as it combines existing sensors and methods without major breakthroughs.
The authors tackled the problem of generating 3D occupancy maps in indoor environments by coupling Intel RealSense T265 and D435i cameras for localization and depth data, resulting in trajectories and a joint point cloud for map building using Octomap representation.
We propose to couple two types of Intel RealSense sensors (tracking T265 and depth D435i) in order to obtain localization and 3D occupancy map of the indoor environment. We implemented a python-based observer pattern with multi-threaded approach for camera data synchronization. We compared different point cloud (PC) alignment methods (using transformations obtained from tracking camera and from ICP family methods). Tracking camera and PC alignment allow us to generate a set of transformations between frames. Based on these transformations we obtained different trajectories and provided their analysis. Finally, having poses for all frames, we combined depth data. Firstly we obtained a joint PC representing the whole scene. Then we used Octomap representation to build a map.