On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
The scientist and engineer's guide to digital signal processing
The scientist and engineer's guide to digital signal processing
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
Observation planning for efficient environment information summarization
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
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In field environments it is not usually possible to provide robots in advance with valid geometric models of its task and environment. The robot or robot teams need to create these models by scanning the environment with its sensors. Here, an information-based iterative algorithm to plan the robot's visual exploration strategy is proposed to enable it to most efficiently build 3D models of its environment and task. The method assumes mobile robot (or vehicle) with vision sensors mounted at a manipulator end-effector (eye-in-hand system). This algorithm efficiently repositions the systems' sensing agents using an information theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map. This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. This map is then distributed among the agents using an information-based relevant data reduction scheme. This method is particularly well suited to unstructured environments, where sensor uncertainty is significant. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulation results show the effectiveness of this algorithm.