Detecting driveable floor regions

Autonomous mobile robots require at least some three dimensional information to navigate in complex and partially unknown environments. Usually it is sufficient to find obstacles in the direction of the planned motion or unobstructed floor space. The distance at which the obstacle detection or floor reconstruction is carried out, determines how far ahead the robot can plan its actions. Covering large space is particularly important in industrial settings and helps in finding quickly right paths and avoiding excessive exploration. In this paper the author presents a novel method of detecting driveable floor regions. The method uses a planar floor model and a vision guided range detection sensor to segment the image into regions and verify which of them belong to the floor. The acceptance criterion uses the sensor error model. The floor detection process requires a relatively small number (in order 25-50) of individual range measurements to plan a safe path for the robot. The author presents experimental results and floor maps created using this method