Simultaneous Localisation And Mapping (SLAM) is a stochastic map building method which permits consistent robot navigation without requiring an a priori map. The map is built incrementally as the robot observes the environment with its on-board sensors and, at the same time, is used to localise the robot. Typically, SLAM has been performed using range-bearing sensors, but the development of a S...

Most research on robot mapping focuses on developing algorithms for mapping with particular sensors, such as laser rangefinders, stereo cameras, or SONAR arrays. In this paper we instead take a broad approach and apply a single mapping algorithm to a large class of sensors with the goal of uncovering relationships between sensing capabilities and map quality. We describe a sensor model which en...

Many cortical maps are found in biological organisms. In these maps, related units tend to be collected close to each other, and important inputs typically have a magniied representation on the map. We consider an interpretation of these structures as maps of parallel information-bearing channels, rather than winner-take-all maps such as the Kohonen self-organizing map (SOM). By considering the...

The Simultaneous Localization And Mapping problem (SLAM) is one of the fundamental ones in mobile robotics. It concerns the incremental construction of an environment model from the fusion of sensory data (laser range finder, vision,. . . ) and proprioceptive data acquired by an inertial measurement unit or by odometry. Sensory data are processed to extract features (interest points, segments, ...

This work describes a knowledge-guided clustering approach for mineral potential mapping (MPM), by which the optimum number of clusters is derived form a knowledge-driven methodology through a concentration-area (C-A) multifractal analysis. To implement the proposed approach, a case study at the North Narbaghi region in the Saveh, Markazi province of Iran, was investigated to discover porphyry ...