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
- SourceAvailable from: Olivier Stasse[show abstract] [hide abstract]
ABSTRACT: We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the "pure vision" domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to Structure from Motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera.IEEE Transactions on Pattern Analysis and Machine Intelligence 07/2007; 29(6):1052-67. · 4.80 Impact Factor
- Journal of Intelligent and Robotic Systems. 01/2009; 54:137-161.
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.Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, April 18-22, 2005, Barcelona, Spain; 01/2005