A dependence maximization approach towards street map-based localization

In this paper, we present a novel approach to 2D street map-based localization for mobile robots that navigate mainly in urban sidewalk environments. Recently, localization based on the map built by Simultaneous Localization and Mapping (SLAM) has been widely used with great success. However, such methods limit robot navigation to environments whose maps are prebuilt. In other words, robots cannot navigate in environments that they have not previously visited. We aim to relax the restriction by employing existing 2D street maps for localization. Finding an exact match between sensor data and a street map is challenging because, unlike maps built by robots, street maps lack detailed information about the environment (such as height and color). Our approach to coping with this difficulty is to maximize statistical dependence between sensor data and the map, and localization is achieved through maximization of a Mutual Information-based criterion. Our method employs a computationally efficient estimator of Squared-loss Mutual Information through which we achieved near real-time performance. The effectiveness of our method is evaluated through localization experiments using real-world data sets.

Authors

• Kiyoshi Irie
• Masashi Sugiyama
• Masahiro Tomono

Keywords

• Robot sensing systems
• Roads
• Yttrium
• Feature extraction
• Buildings
• Navigation
• Urban Environments
• Sensor Data
• Mutual Information
• Local Experience
• Real-world Datasets
• Road Map
• Mobile Robot
• Statistical Dependence
• Simultaneous Localization And Mapping
• Robot Navigation
• Root Mean Square Error
• Prior Information
• Position Error
• Particle Filter
• Position Tracking
• Motion Estimation
• Boundary Line
• Grid Map
• Error Map
• Road Boundary
• Position Of The Robot
• Odometry
• Place In Environments
• Robot Localization
• Real-time Kinematic
• Laser Measurement
• Ground Truth Position
• Autonomous Navigation
• Normalized Cross-correlation