SLAM auto-complete using an emergency map

We present a graph representation that fuses information from the robot sensory system and an emergency-map into one. Emergency-maps are a support extensively used by firemen in rescue mission. Enabling the robot to use such prior maps instead of starting SLAM from scratch will aid planning and navigation for robots in new environments. However, those maps can be outdated, information might be missing, and the scales of all rooms are typically not consistent. To be able to use them during robot navigation while correcting the map's errors is an important problem for firemen. We use corners as a common landmark, between the emergency and the robot-map, to create a graph associating information from both types of maps. The graph is optimized, using a combination of robust kernels, fusing information from the emergency and the robot-map into one map even when faced with scale inaccuracies and inexact start poses. Experiments on an office environment show that, we can handle up to 71% of wrong correspondences and still get the expected results. By incorporating the emergency-map's information in the robot-map, the robot can navigate and explore while taking into account places it hasn't yet seen. We also show that the emergency-map is enhanced by adding information not represented such as closed doors or walls not represented.

Comments: 8 pages, 10 figures

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