Selecting landmarks in novel environments

Selecting Landmarks for Localization in Natural Terrain

We describe techniques to optimally select landmarks for performing mobile robot localization by matching terrain maps. The method is based upon a maximum-likelihood robot localization algorithm that efficiently searches the space of possible robot positions. We use a sensor error model to estimate a probability distribution over the terrain expected to be seen from the current robot position. ...

Toward selecting and recognizing natural landmarks

Landmarks are often used as a basis for mobile robot navigation. In this paper, we consider the problem of automatically selecting from a set of 3D features the subset which is most likely to be recognized from noisy monocular image data and is least likely to be confused with any other group of features. Assuming perspective projection, real valued recognition functions are constructed for a s...

Representing and Selecting Landmarks in Autonomous Learning of Robot Navigation

Navigation based on detected landmarks is an important facet of robot navigation. This work investigates into a qualitative representation of landmarks for an autonomous learning task where a robot learns a goal directed navigation strategy with reinforcement learning. We discuss how to build a suitable landmark-based representation. In particular, we focus on selection of landmarks to regard w...

Selecting landmarks when giving directions to different addressees on campus

Landmarks can be helpful guides when completing spatial tasks, such as giving directions. Past research has shown that the salience of landmarks can influence their use, with salience based on perceptual features, or on the spatial relation between the landmark and a target location. In two experiments, students were asked to give directions to locations on campus to other students, alumni, or ...

Selecting Landmarks from Sonar Scans for Mobile Robot Navigation