Work

Simultaneous localisation and mapping from natural landmarks using RatSLAM

Year: 2004

Type: conference-paper

Abstract: This paper describes the current state of RatSLAM, a Simultaneous Localisation and Mapping (SLAM) system based on models of the rodent hippocampus. RatSLAM uses a competitive attractor network to fuse visual and odometry information. Energy packets in the network represent pose hypotheses, which are updated by odometry and can be enhanced or inhibited by visual input. This paper shows the effectiveness of the system in real robot tests in unmodified indoor environments using a learning vision system. Results are shown for two test environments; a large corridor loop and the complete floor of an office building.

Source: QUT ePrints (Queensland University of Technology)

Authors: Michael Milford, Gordon Wyeth, David Prasser

Institution: The University of Queensland

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Commonwealth Scientific and Industrial Research Organisation

OpenAlex ID: W1484169642