Cooperative longterm SLAM for navigating mobile robots in industrial applications

Abstract

Precise and reliable localization as well as dynamic path planning are key components to enable flexibly and efficiently operating mobile robots in industrial applications. Both strongly depend on up-to-date navigation maps of the respective environment. However, in these particular applications, providing those maps can be very challenging due to the typical dynamics and size of the environment. Promising approaches tackle the issue of localization in dynamic environments by estimating an update of the map while simultaneously localizing in it. In order to have a good estimate of the dynamics of the environment and update the map accordingly, frequent observations of all areas of the environment are required. This is often not possible, especially in large environments and from a single robot's perspective. To overcome this problem, we present a cooperative approach which uses the sensor information of all mobile robots and possibly available stationary sensors to generate an up-to-date global map and precisely localize the robots within it. We use dynamic occupancy grid maps with Rao-Blackwellized particle filters in combination with a suitable server-agent architecture to allow cooperation. The advantage of our approach is shown both in simulation and on real hardware.