Generating Octrees from Object Silhouettes in Orthographic Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Uncertainty to Visual Exploration
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Finding the parts of objects in range images
Computer Vision and Image Understanding
Occlusions as a Guide for Planning the Next View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Darboux Frames, Snakes, and Super-Quadrics: Geometry from the Bottom Up
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Sequential Determination of Model Misfit
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraint-Based Sensor Planning for Scene Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Object Recognition: Looking for Differences
International Journal of Computer Vision - Special issue: Research at McGill University
On the Sequential Accumulation of Evidence
International Journal of Computer Vision - Special issue: Research at McGill University
Selecting Landmarks for Localization in Natural Terrain
Autonomous Robots
Simultaneous Localization and Map-Building Using Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
View planning for automated three-dimensional object reconstruction and inspection
ACM Computing Surveys (CSUR)
Experimental comparison of superquadric fitting objective functions
Pattern Recognition Letters
View planning for BRDF acquisition
ACM SIGGRAPH 2003 Sketches & Applications
A CAD-based 3D data acquisition strategy for inspection
Machine Vision and Applications
Evolutionary computation for sensor planning: the task distribution plan
EURASIP Journal on Applied Signal Processing
Metric embedding of view-graphs
Autonomous Robots
Trajectory Optimization using Reinforcement Learning for Map Exploration
International Journal of Robotics Research
Inter-Image Statistics for 3D Environment Modeling
International Journal of Computer Vision
Machine Vision and Applications
A New View Planning Method for Automatic Modeling of Three Dimensional Objects
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
Exploring unknown environments with mobile robots using coverage maps
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Developing visual sensing strategies through next best view planning
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Trajectories tracing for a pitching robot based on human recognition
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Computations on a spherical view space for efficient planning of viewpoints in 3-D object modeling
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
An autonomous six-DOF eye-in-hand system for in situ 3D object modeling
International Journal of Robotics Research
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Active planning for underwater inspection and the benefit of adaptivity
International Journal of Robotics Research
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Passively accepting measurements of the world is not enough, as the data we obtain is always incomplete, and the inferences made from it uncertain to a degree which is often unacceptable. If we are to build machines that operate autonomously, they will always be faced with this dilemma, and can only be successful if they play a much more active role. This paper presents such a machine. It deliberately seeks out those parts of the world which maximize the fidelity of its internal representations, and keeps searching until those representations are acceptable. We call this paradigm autonomous exploration, and the machine an autonomous explorer.This paper has two major contributions. The first is a theory that tells us how to explore, and which confirms the intuitive ideas we have put forward previously. The second is an implementation of that theory. In our laboratory, we have constructed a working autonomous explorer and here, for the first time, show it in action. The system is entirely bottom-up and does not depend on any a priori knowledge of the environment. To our knowledge, it is the first to have successfully closed the loop between gaze planning and the inference of complex 3D models.