On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Frontier-based exploration using multiple robots
AGENTS '98 Proceedings of the second international conference on Autonomous agents
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Merging Gaussian Distributions for Object Localization in Multi-robot Systems
ISER '00 Experimental Robotics VII
Toward selecting and recognizing natural landmarks
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 1 - Volume 1
Vision-Based Pose Computation: Robust and Accurate Augmented Reality Tracking
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
Visually Guided Cooperative Robot Actions Based on Information Quality
Autonomous Robots
An information-based exploration strategy for environment mapping with mobile robots
Robotics and Autonomous Systems
Autonomous robot manipulator-based exploration and mapping system for bridge maintenance
Robotics and Autonomous Systems
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
Autonomous tactile perception: A combined improved sensing and Bayesian nonparametric approach
Robotics and Autonomous Systems
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In field environments it is often not possible to provide robot teams with detailed a priori environment and task models. In such unstructured environments, robots will need to create a dimensionally accurate three-dimensional geometric model of its surroundings by performing appropriate sensor actions. However, uncertainties in robot locations and sensing limitations/occlusions make this difficult. A new algorithm, based on iterative sensor planning and sensor redundancy, is proposed to build a geometrically consistent dimensional map of the environment for mobile robots that have articulated sensors. The aim is to acquire new information that leads to more detailed and complete knowledge of the environment. The robot(s) is controlled to maximize geometric knowledge gained of its environment using an evaluation function based on Shannon's information theory. Using the measured and Markovian predictions of the unknown environment, an information theory based metric is maximized to determine a robotic agent's next best view (NBV) of the environment. Data collected at this NBV pose are fused using a Kalman filter statistical uncertainty model to the measured environment map. The process continues until the environment mapping process is complete. The work is unique in the application of information theory to enhance the performance of environment sensing robot agents. It may be used by multiple distributed and decentralized sensing agents for efficient and accurate cooperative environment modeling. The algorithm makes no assumptions of the environment structure. Hence, it is robust to robot failure since the environment model being built is not dependent on any single agent frame, but is set in an absolute reference frame. It accounts for sensing uncertainty, robot motion uncertainty, environment model uncertainty and other critical parameters. It allows for regions of higher interest receiving greater attention by the agents. This algorithm is particularly well suited to unstructured environments, where sensor uncertainty and occlusions are significant. Simulations and experiments show the effectiveness of this algorithm.