Visual place categorization in maps

Categorizing areas such as rooms and corridors using a discrete set of labels has been of long-standing interest to the robotics community. A map with labels such as kitchen, lab, copy room etc provides a basic amount of semantic information that can enable a robot to perform a number of tasks specified in human-centric terms rather than just map coordinates. In this work, we propose a method to label areas in a pre-built map using information from camera images. In contrast to most existing approaches, our method labels the area that is viewed in the camera image rather than just the current robot location. Place labels are generated from the image input using the PLISS system [14]. The label information on the viewed areas is integrated in a Conditional Random Field (CRF) that also considers higher level semantics such as adjacency and place boundaries. We demonstrate our technique on maps built using from laser and visual SLAM systems. We obtain the correct place categorization of a very high percentage of the map areas even when the place categorization system is trained using images only from the internet.

Authors

• Ananth Ranganathan
• Jongwoo Lim

Keywords

• Labeling
• Lasers
• Visualization
• Cameras
• Robot kinematics
• Streaming media
• Visual Classification
• Input Image
• Camera Images
• Map Of Area
• Conditional Random Field
• Robot Localization
• High-level Semantics
• Visual Simultaneous Localization And Mapping
• Laser Scanning
• Hidden Markov Model
• Visual Features
• Image Area
• Neighboring Cells
• Grid Cells
• Current Image
• Online System
• Maximum A Posteriori
• Grid Map
• Ray Tracing
• Accurate Labels
• Environment Map
• Nonexpansive Mapping
• Change Point Detection
• Topological Map
• Robot Trajectory
• Image Stream
• Label Probability
• Spatial Consistency
• Web Map
• Label Distribution