Hippocampal models for simultaneous localisation and mapping on an autonomous robot
Milford, Michael & Wyeth, Gordon (2003) Hippocampal models for simultaneous localisation and mapping on an autonomous robot. In Roberts, Jonathan & Wyeth, Gordon (Eds.) Proceedings of the Australasian Conference on Robotics and Automation, 2003, Australian Robotics and Automation Association Inc, Brisbane, Queensland.
To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but
there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic
manner. The system can effectively perform parameter self-calibration and SLAM in one dimension. Tests in two dimensional environments revealed the inability of the
RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)|
|Copyright Owner:||Copyright 2003 [please consult the authors]|
|Deposited On:||23 Jun 2010 10:22|
|Last Modified:||23 Jun 2010 10:22|
Repository Staff Only: item control page