Terrain Mapping for a Roving Planetary Explorer

The main task of perception for autonomous vehicles is to build a representation of the observed environment in order to carry out a mission. In particular, terrain modeling, that is modeling the geometry of the environment observed by the vehicle's semors, is crucial for autonomous underwater exploration. The purpose of this work is to analyze the components of the terrain modeling task, to investigate the algorithms and representations for this task, and to evaluate them in the context of real applications. Terrain representation is an issue that is of interest in many areas of mobile robotics, such as land vehicles, planetary explorers, etc. This paper surveys some of the ideas developed in those areas and their relevance to the underwater navigation problem. Terrain modeling is divided into three parts: structuring sensor data, extracting features, and merging and updating terrain models

Methods for Identifying Footfall Positions for a Legged Robot

We are designing a complete autonomous legged robot to perfom planetary exploration without human supervision. This robot must traverse unknown and geographically diverse areas in order a collect samples of materials. This paper describes how a geometric terrain representation from range imagery can be used to identify footfall positions. First, we present previous research aimed to determine footfall positions. Second, we describe several methods for determining the positions for which the shape of the terrain is nearest to the shape of the foot. Third, we evaluate and compare the efficiency of these methods as functions of some parameters such as particularities of the shape of the terrain. Fourth, we introduce other methods that use thermal imaging in order to differentiate material

First Results in Terrain Mapping for a Roving Planetary Explorer

To perform planetary exploration without human supervision, a complete autonomous rover must be able to model its environment while exploring its surroundings. We present a new algorithm to construct a geometric terrain representation from a single range image. The form of the representation is an elevation map that includes uncertainty, unknown areas, and local features. By virtue of working in spherical-polar space, the algorithm is independent of the desired map resolution and the orientation of the sensor, unlike other algorithms that work in Cartesian space. We also describe new methods to evaluate regions of the constructed elevation maps to support legged locomotion over rough terrain