This paper discusses a sketch interface that can be used to guide a mobile robot along a specified path in its unfamiliar place. With the sketch interface, the user draws a rough map to give navigation tasks to robots. Because sketched maps often suffer from various inaccuracies and large errors in landmarks, we discuss what kinds of uncertainties in the rough maps would mainly have effects on navigating a robot. The effects of such inaccuracies on robot navigation are analyzed in simulated environments. A quantitative navigability measure of rough maps is then developed based on the analysis. Experimental results are also presented for validating the navigability measure.

We describe a method of mobile robot localization based on a rough map using stereo vision, which uses multiple visual features to detect and segment the buildings in the robot's field of view. The rough map is an inaccurate map with large uncertainties in the shapes, the dimensions and the locations of objects so that it can be built easily. The robot fuses odometry and vision information using extended Kalman filters to update the robot pose and the associated uncertainty based on the recognition of buildings in the map. We use multi-hypothesis Kalman filter to generate and track Gaussian pose hypotheses. An experimental result shows the feasibility of our localization method in an outdoor environment.