Monte Carlo Localization using 3D texture maps

This paper uses KLD-based (Kullback-Leibler Divergence) Monte Carlo Localization (MCL) to localize a mobile robot in an indoor environment represented by 3D texture maps. A 3D texture map is a simplified model that includes vertical planes with colored texture information associated with each vertical plane. At each time step, a distance measurement and an observed texture from an omnidirectional camera are compared to the expected distance measurement and the expected texture according to each hypothesis of the robot's pose in an MCL framework. Compared to previous implementations of MCL, our proposed approach converges faster than distance-only MCL and localizes the robot more precisely than SIFT-based MCL. We demonstrate this new MCL algorithm for robot localization with experiments in several hallways.

Authors

• Yu Fu
• Stephen Tully
• George Kantor
• Howie Choset

Keywords

• Robot sensing systems
• Three dimensional displays
• Particle measurements
• Atmospheric measurements
• Cameras
• Texture Map
• Monte Carlo Localization
• Distancing Measures
• Kullback-Leibler
• Indoor Environments
• Vertical Plane
• Similarity Measure
• Weight Ratio
• Measurement Model
• Image Pixels
• Azimuth Angle
• Tilt Angle
• Local Approach
• Localization Results
• Motion Model
• Particle Filter
• Extended Kalman Filter
• Maximum Radius
• Odometry
• Particle Weight
• Pose Tracking
• Boundary Pixels
• Global Localization
• Blue Dotted Line
• Robot Pose
• Height Of Point
• Laser Ranging