Uncertain map making in natural environments
ABSTRACT Building on previous work on incremental natural scene modelling
for mobile robot navigation, we focus in this paper on the problem of
representing and managing uncertainties. The environment is composed of
ground regions and objects. Objects (e.g., rocks) are represented by an
uncertain state vector (location) and a variance-covariance matrix.
Their shapes are approximated by ellipsoids. Landmarks are defined as
objects with specific properties (discrimination, accuracy) that permit
to use them for robot localization and for anchoring the environment
model. Model updating is based on an extended Kalman filter.
Experimental results are given that show the construction of a
consistent model over tens of meters
Conference Proceeding: Autonomous Terrain Mapping and Classification Using Hidden Markov Models[show abstract] [hide abstract]
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.Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on; 05/2005
Conference Proceeding: Visually built task models for robot teams in unstructured environments[show abstract] [hide abstract]
ABSTRACT: In field environments it is not usually possible to provide robotic systems with valid geometric models of the task and environment. The robot or robot teams will need to create these models by performing appropriate sensor actions. Here, an algorithm based on iterative sensor planning and sensor redundancy is proposed to enable them to efficiently build 3D models of the environment and task. The method assumes stationary robotic vehicles with cameras carried by articulated mounts. The algorithm uses the measured scene information to find new camera mount poses based on information content. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulations show the effectiveness of this algorithm.Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International Conference on; 02/2002
Journal of Intelligent and Robotic Systems. 01/2009; 54:137-161.