Learning to Select Useful Landmarks

Learning to Select Useful Landmarks

Active manifold learning via a unified framework for manifold landmarking

The success of semi-supervised manifold learning is highly dependent on the quality of the labeled samples. Active manifold learning aims to select and label representative landmarks on a manifold from a given set of samples to improve semi-supervised manifold learning. In this paper, we propose a novel active manifold learning method based on a unified framework of manifold landmarking. In par...

Learning to Select Useful

To navigate eeectively, an autonomous agent must be able to quickly and accurately determine its current location. Given an initial estimate of its position (perhaps based on dead-reckoning) and an image taken of a known environment, our agent rst attempts to locate a set of landmarks (real-world objects at known locations), then uses their angular separation to obtain an improved estimate of i...

Learning to Autonomously Select Landmarks for Navigation and Communication

Selecting landmarks for use by a navigating mobile robot is important for map-building systems. However, it can also provide a way by which robots can communicate route information, so that one robot can tell another how to find a goal location. A route through an environment can be described by the landmarks encountered along the path, and a robot following the same path must identify the perc...

Gaze patterns in navigation: encoding information in large-scale environments.

We investigated the role of gaze in encoding of object landmarks in navigation. Gaze behavior was measured while participants learnt to navigate in a virtual large-scale environment in order to understand the sampling strategies subjects use to select visual information during navigation. The results showed a consistent sampling pattern. Participants preferentially directed gaze at a subset of ...