For a robot to plan a path from one point to another, it needs a map of the environment. Simultaneous localization and mapping (SLAM) allows a robot to build a map online and, at the same time, localize itself within that map. SLAM accounts for uncertainties and errors that arise from imperfect sensing and actuation. However, in crowded environments, additional challenges arise. Dynamic obstacles in the sensor field of view can be wrongly incorporated into the map. They can also cause the robot to become lost if there are too many occlusions.
In CROWDBOT, we take an active SLAM approach which balances the robot’s need to maintain good localization within parts of the map that it has already seen (i.e. areas with fewer dynamic obstacles), and the need to explore new areas to complete the map. We also conduct a pre-filtering step over the incoming sensor scans, in our case 360° 2D LIDAR, to remove points that are deemed to be returns from dynamic obstacles rather than from the static environment. This way, the robot builds clean, coherent maps that can be used for navigation.