Autonomous Terrain Mapping and Classification Using Hidden Markov Models

This paper presents a new approach for terrain mapping and classification using mobile robots with 2D laser range finders. Our algorithm generates 3D terrain maps and classifies navigable and non-navigable regions on those maps using Hidden Markov models. The maps generated by our approach can be used for path planning, navigation, local obstacle avoidance, detection of changes in the terrain, and object recognition. We propose a map segmentation algorithm based on Markov Random Fields, which removes small errors in the classification. In order to validate our algorithms, we present experimental results using two robotic platforms.

Authors

• Denis Fernando Wolf
• Gaurav S. Sukhatme
• Dieter Fox
• Wolfram Burgard

Keywords

• Terrain mapping
• Hidden Markov models
• Path planning
• Navigation
• Mobile robots
• Application software
• Robot sensing systems
• Computer science
• Clouds
• Laboratories
• Hidden Markov Model
• Terrain Map
• Terrain Classification
• Autonomous Mapping
• Change Detection
• Mapping Approach
• Object Recognition
• Classification Approach
• Classification Error
• Mobile Robot
• Segmentation Map
• Markov Random Field
• Laser Ranging
• Grid Cells
• Point Cloud
• Pose Estimation
• Sequence Of Points
• Sequence Of States
• Range Of Sensors
• Odometer
• Autonomous Navigation
• Robotics Community
• Flat Terrain
• Sensor Noise
• Segmentation Techniques
• Part Of The Map
• Neighboring Points