On measuring the accuracy of SLAM algorithms
Autonomous Robots
Recognizing places using spectrally clustered local matches
Robotics and Autonomous Systems
Real-time correlative scan matching
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A comparison of SLAM algorithms based on a graph of relations
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Study of parameterizations for the rigid body transformations of the scan registration problem
Computer Vision and Image Understanding
Large scale graph-based SLAM using aerial images as prior information
Autonomous Robots
CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory
International Journal of Robotics Research
The University of Pennsylvania MAGIC 2010 multi-robot unmanned vehicle system
Journal of Field Robotics
Progress toward multi-robot reconnaissance and the MAGIC 2010 competition
Journal of Field Robotics
Information-theoretic compression of pose graphs for laser-based SLAM
International Journal of Robotics Research
Exploration and mapping with autonomous robot teams
Communications of the ACM
Lifelong localization in changing environments
International Journal of Robotics Research
Hi-index | 0.02 |
Mobile robots are dependent upon a model of the environment for many of their basic functions. Locally accurate maps are critical to collision avoidance, while large-scale maps (accurate both metrically and topologically) are necessary for efficient route planning. Solutions to these problems have immediate and important applications to autonomous vehicles, precision surveying, and domestic robots. Building accurate maps can be cast as an optimization problem: find the map that is most probable given the set of observations of the environment. However, the problem rapidly becomes difficult when dealing with large maps or large numbers of observations. Sensor noise and non-linearities make the problem even more difficult—especially when using inexpensive (and therefore preferable) sensors. This thesis describes an optimization algorithm that can rapidly estimate the maximum likelihood map given a set of observations. The algorithm, which iteratively reduces map error by considering a single observation at a time, scales well to large environments with many observations. The approach is particularly robust to noise and non-linearities, quickly escaping local minima that trap current methods. Both batch and online versions of the algorithm are described. In order to build a map, however, a robot must first be able to recognize places that it has previously seen. Limitations in sensor processing algorithms, coupled with environmental ambiguity, make this difficult. Incorrect place recognitions can rapidly lead to divergence of the map. This thesis describes a place recognition algorithm that can robustly handle ambiguous data. We evaluate these algorithms on a number of challenging datasets and provide quantitative comparisons to other state-of-the-art methods, illustrating the advantages of our methods. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)