|Volume 23 Issue 4/5- Publication Date: 1 April-May 2004|
|Special Issue on the 8th International Symposium on Experimental Robotics (ISER ’02)|
|Navigation and Mapping in Large Unstructured Environments|
|J. Guivant, E. Nebot, ARC Centre of Excellence in Autonomous Systems (CAS), Australian Centre for Field Robotics, University of Sydney, SW, Australia and CRC Mining J. Nieto ARC Centre of Excellence in Autonomous Systems (CAS), Australian Centre for Field Robotics, University of Sydney, NSW, Australia and F. Masson Laboratorio de Control y Robotica, Universidad Nacional del Sur, Argentina|
In this paper we address the problem of autonomous navigation in very large unstructured environments. A new hybrid metric map (HYMM) structure is presented that combines feature maps with other metric representations in a consistent manner. The global feature map is partitioned into a set of connected local triangular regions (LTRs), which provide a reference for a detailed multidimensional description of the environment. The HYMM framework permits the combination of efficient feature-based simultaneous localization and mapping (SLAM) algorithms for localization with, for example, occupancy grid maps for tasks such as obstacle avoidance, path planning or data association. This fusion of feature and grid maps has several complementary properties; for example, grid maps can assist data association and can facilitate the extraction and incorporation of new landmarks as they become identified from multiple vantage points. In this paper we also present a path-planning technique that efficiently maintains the estimated cost of traversing each LTR. The consistency of the SLAM algorithm is investigated with the introduction of exploration techniques to guarantee a certain measure of performance for the estimation process. Experimental results in outdoor environments are presented to demonstrate the performance of the algorithms proposed.
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