Place Cells in Head-Fixed Mice Navigating a Floating Real-World Environment

Knowing Your Place in the Real World

This paper addresses the scalability of existing techniques for mobile robot navigation to work in large, unstructured environments. In particular, we are interested in the problem of relocalisation after the robot has been placed at some arbitrary location (unknown to the robot) in a previously explored environment. In the rst instance, we assess the problems found when a system previously sho...

Knowing Your Place in Real World Environments

The topic of mobile robot self-localisation is usually divided into the sub-problems of global localisation and position tracking. Both are now well understood individually, but few mobile robots can deal simultaneously with the two problems in large, complex environments. While eecient solutions have been found for metric maps, topological maps have, by nature of their compactness, the potenti...

Place Learning in Dynamic Real-World Environments

In this paper, we present an approach for mobile robot localization designed for use in dynamic environments. Our approach integrates evidence grids within a topo-logical/metric network that can be used for navigation. Place learning consists of associating evidence grids with places in the topological network. Place recognition consists of building an evidence grid at the current location and ...

Navigating in a 3D world

Gait parameters while walking in a head-mounted display virtual environment and the real world

Full-body motion tracking data was collected for six subjects during free walking. Each participant was asked to walk to a previously seen target under four experimental conditions: eyes closed within the real world, eyes closed wearing a head-mounted display (HMD), eyes open in the real world, and eyes open wearing a HMD. We report three gait parameters for each of these four conditions: strid...