Section: New Results

A unified framework for robust 2D/3D PML-SLAM

Participants : Kathia Melbouci, Fawzi Nashashibi.

Enhancing the outdoor mapping with SLAM based approaches is still an active research area. The main reason is that a consistent map of the vehicle's surrounding is one of the prerequisites for an effective vehicle interaction with this environment. In this context, and for the VALET project purpose, we have extended the PML-SLAM framework to handle 2D and 3D Lidars by replacing the localization module and designing a sparse pose graph optimizer. The sparse pose graph jointly optimizes the poses of the submaps generated by the local SLAM, which are already used for the mapping task, and the poses of the scans estimated following the scan matching process. This optimization is formulated as a non linear least square problem, and runs online in a background thread. The optimized poses are used to correct the vehicle's trajectory and to update the environment map. Furthermore, the graph-based PML-SLAM can deal with different sensors (IMU, GPS), that is, a sensor fusion "Kalman-filter" based is available to provide a good pose estimate for the local SLAM.