6 accepted papers
Range-based SLAM is a well-established technique for estimating the pose of a mobile robot operating in an unknown environment. Current state-of-the-art solutions use occupancy-grid maps to represent the world. While fast and accurate, their performance is limited by two facts. First, in an occupanc
Recovering the 3D structure of the surrounding environment is an essential task in any vision-controlled Structure-from-Motion (SfM) scheme. This paper focuses on the theoretical properties of the SfM, known as the incremental active depth estimation. The term incremental stands for estimating the 3…
In this paper, we propose a mapping technique that builds a continuous representation of the environment from range data. The strategy presented here encodes the probability of points in space to be occupied using 2.5D B-spline surfaces. For a fast update rate, the surface is recursively updated as…
The 3D depth estimation and relative pose estimation problem within a decentralized architecture is a challenging problem that arises in missions that require coordination among multiple vision-controlled robots. The depth estimation problem aims at recovering the 3D information of the environment.…
This paper presents a 2D B-spline mapping framework for representing unstructured environments in a compact manner. While occupancy-grid and landmark-based maps have been successfully employed by the robotics community in indoor scenarios, outdoor long-term autonomous operations require a more compa…
This letter proposes a low-level visual navigation algorithm to improve visual localization of a mobile robot. The algorithm, based on artificial potential fields, associates each feature in the current image frame with an attractive or neutral potential energy, with the objective of generating a co