Abstract-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. Autonomous navigation is a fundamental capability for a mobile robot. When traversing rough terrain, the robot must have the ability to avoid not only obstacles but also parts of the terrain that are considered not safe for navigation [1] . In this paper, we present an online algorithm that builds a 3D map of the terrain and classifies the mapped regions as navigable or non-navigable areas. The maps created by our approach have numerous applications such as: local avoidance of non-navigable areas, path planning, and object matching and recognition. We are particularly interested in the first two applications. Outdoor 3D maps have been addressed by the computer vision community for many years Different methods have been used to create the 3D representations of the environment such as: point clouds [7] , triangular meshes, and planar structures [10] . Our approach uses point clouds to represent the terrain. This method can represent fine details of the environment and it is also easy and fast to compute. On the other hand, it requires considerable memory space to represent large areas. Part of the 3D outdoor mapping effort by the robotics community is focused on the 3D terrain mapping problem. This is an important problem when one is exploring unknown terrain. Applications for terrain mapping range from path planning and local obstacle avoidance to detection of changes
HMM can also be applied to Lidar Mapping for obstacle avoidance, discerning navigable area from non-navigable area REF .
Autonomous Terrain Mapping and Classification Using Hidden Markov Models
Proceedings of the 2005 IEEE International Conference on Robotics and Automation
Proceedings of the 2005 IEEE International Conference on Robotics and Automation
Engineering Computer Science