A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sensor planning for 3D object search
Computer Vision and Image Understanding
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
International Journal of Computer Vision
HERB: a home exploring robotic butler
Autonomous Robots
High-accuracy 3D sensing for mobile manipulation: improving object detection and door opening
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World
International Journal of Robotics Research
Modeling 3d objects from stereo views and recognizing them in photographs
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Topological spatial relations for active visual search
Robotics and Autonomous Systems
Contextually guided semantic labeling and search for three-dimensional point clouds
International Journal of Robotics Research
Autonomous Robots
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This article presents a systematic approach to the problem of autonomous 3D object search in indoor environments, using a two-wheeled non-holonomic robot equipped with an actuated stereo-camera head and processing done on a single laptop. A probabilistic grid-based map encodes the likelihood of object existence in each cell and is updated after each sensing action. The updating schema incorporates characteristic parameters modeled after the robot's sensing modalities and allows for sequential updating via Bayesian recursion methods. Two types of sensing modalities are used to update the map: a coarse search method (global search) based on a color histogram approach, and a more refined search method (local search) based on Scale-Invariant Feature Transform (SIFT) feature matching. If the local search correctly locates the desired object, its 6-DOF pose is estimated using stereo applied to each SIFT feature (i.e. 3D SIFT feature), which is then fed as measurements into an Extended Kalman Filter (EKF) for sustained tracking. If the local search fails to locate the desired object in a particular cell, the cell is updated in the probability map and the next peak probability cell is identified and planned to using a separate grid-based costmap populated via obstacle detection from stereo, with planning done using an A* planner. Experimental results obtained from the use of this method on a mobile robot are presented to illustrate and validate the approach, confirming that the search strategy can be carried out with modest computation on a single laptop.