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Navigation and Mapping in Large Unstructured Environments

ARC Centre of Excellence in Autonomous Systems (CAS) Australian Centre for Field Robotics University of Sydney, NSW, Australia and CRC Mining

Eduardo Nebot

ARC Centre of Excellence in Autonomous Systems (CAS) Australian Centre for Field Robotics University of Sydney, NSW, Australia and CRC Mining nebot{at}cas.edu.au

Juan Nieto

ARC Centre of Excellence in Autonomous Systems (CAS) Australian Centre for Field Robotics University of Sydney, NSW, Australia

Favio 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.

Key Words: autonomous vehicles • SLAM • mapping • Kalman filter

The International Journal of Robotics Research, Vol. 23, No. 4-5, 449-472 (2004)
DOI: 10.1177/0278364904042203


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