ARK: Autonomous mobile robot in an industrial environment

This paper describes research on the ARK (Autonomous Mobile Robot in a Known Environment) project. The technical objective of the project is to build a robot that can navigate in a complex industrial environment using maps with permanent structures. The environment is not altered in any way by adding easily identifiable beacons and the robot relies on naturally occurring objects to use as visual landmarks for navigation. The robot is equipped with various sensors that can detect unmapped obstacles, landmarks and objects. In this paper we describe the robot's industrial environment, it's architecture, a novel combined range and vision sensor and our recent results in controlling the robot in the real-time detection of objects using their color and in the processing of the robot's range and vision sensor data for navigation

Efficient Closed-Loop Detection and Pose Estimation for Vision-Only Relative Localization in Space with a Cooperative Target

Copyright © 2014 by the authors, published by the American Institute of Aeronautics and Astronautics, Inc.Presented at AIAA Space and Astronautics Forum and Exposition, San Diego, CA, August 4-7, 2014.DOI: http://dx.doi.org/10.2514/6.2014-4262An integrated processing pipeline is presented for relative pose estimation of vision-only cooperative localization between two vehicles with unknown relative motion. The motivating scenario is that of proximity operations between two spacecraft when the target spacecraft has a special target pattern and the chase spacecraft is navigating using only a monocular visual sensor. The only prior information assumed is knowledge of the target pattern, which we propose to consist of nested circular blobs. The algorithm is useful for applications requiring localization accuracy using limited computational resources. It achieves low computational cost with high accuracy and robustness via the following contributions: (1) an adaptive visual pattern detection scheme based on the estimated relative pose, which improves both the e ciency of detection and accuracy of pose estimates; (2) a parametric blob detector called Box-LoG which is computationally e cient; and (3) an algorithm which jointly solves the frame-to-frame data association and relative pose estimation. An incremental smoothing technique temporally smooths the pose estimates. The approach can deal with target re-acquisition after loss of the target pattern from the field of view. The algorithm is tested in both synthetic simulations and on an actual spacecraft simulator platform