|Volume 25 Issue 8 - Publication Date: 1 August 2006
|DenseSLAM: Simultaneous Localization
and Dense Mapping
|Juan Nieto, Jose Guivant
and Eduardo Nebot ARC Centre of Excellence for Autonomous
Systems (CAS), The University of Sydney, NSW, Australia
|This paper addresses the problem
of environment representation for Simultaneous Localization and Mapping
(SLAM) algorithms. One of the main problems of SLAM is how to interpret
and synthesize the external sensory information into a representation of
the environment that can be used by the mobile robot to operate autonomously.
Traditionally, SLAM algorithms have relied on sparse environment representations.
However, for autonomous navigation, a more detailed representation of the
environment is necessary, and the classic feature-based representation fails
to provide a robot with sufficient information. While a dense representation
is desirable, it has not been possible for SLAM paradigms.
|This paper presents Dense-SLAM,
an algorithm to obtain and maintain detailed environment representations.
The algorithm represents different sensory information in dense multi-layered
maps. Each layer can represent different properties of the environment,
such as occupancy, traversability, elevation or each layer can describe
the same environment property using different representations. Implementations
of the algorithm with two different representations for the dense maps are
|A rich representation has several
potential advantages to assist the navigation process, for example to facilitate
data association using multi-dimensional maps. This paper presents two particular
applications to improve the localization process; the extraction of complex
landmarks from the dense maps and the detection of areas with dynamic objects.
The paper also presents an analysis of consistency of the maps obtained
with Dense-SLAM. The position error in the dense maps
|The algorithm was tested with
outdoor experimental data taken with a ground vehicle. The experimental
results show that the algorithm can obtain dense environment representations
and that the detailed representation can be used to improve the vehicle