Navigation and mapping in large unstructured environments

JOSE GUIVANT; JUAN NIETO; FAVIO MASSON; EDUARDO NEBOT

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH

SAGE PUBLICATIONS LTD

Lugar: London; Año: 2004 vol. 23 p. 449 - 472

0278-3649

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 triangu- lar regions (LTRs), which provide a reference for a detailed multi- dimensional description of the environment. The HYMM framework permits the combination of efficient feature-based simultaneous lo- calization and mapping (SLAM) algorithms for localization with, for example, occupancy grid maps for tasks such as obstacle avoid- ance, 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 mul- tiple 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 cer- tain measure of performance for the estimation process. Experimen- tal results in outdoor environments are presented to demonstrate the performance of the algorithms proposed.