This paper describes a method of simultaneously estimating the road region and the ego-motion for outdoor mobile robots. Temporal integration of sensor data is effective for robust estimation of road region. To integrate sensor data obtained at multiple places, the robot's ego-motion has to be estimated simultaneously. It is also necessary to use multiple sensors for reliable estimation because road boundary features from one sensor, such as white lines and curbs, are not always available. In addition, to cope with the change of the road type, we prepare multiple road models for estimation. We implement this multi sensor-based, simultaneous estimation of road region and ego-motion using a particle filter. We also devise a technique for generating new particles to cope with gradual road type changes. The proposed method has been successfully applied to autonomous navigation in various road scenes. Application to other types of roads such as intersections is also discussed.
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