Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Fast Discriminant Approach to Active Object Recognition and Pose Estimation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Efficient Discriminant Viewpoint Selection for Active Bayesian Recognition
International Journal of Computer Vision
Planning Algorithms
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
A point-and-click interface for the real world: laser designation of objects for mobile manipulation
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Curious George: An attentive semantic robot
Robotics and Autonomous Systems
The Journal of Machine Learning Research
A fast data collection and augmentation procedure for object recognition
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Contextually guided semantic labeling and search for three-dimensional point clouds
International Journal of Robotics Research
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Object detection is a basic skill for a robot to perform tasks in human environments. In order to build a good object classifier, a large training set of labeled images is required; this is typically collected and labeled (often painstakingly) by a human. This method is not scalable and therefore limits the robot's detection performance. We propose an algorithm for a robot to collect more data in the environment during its training phase so that in the future it could detect objects more reliably. The first step is to plan a path for collecting additional training images, which is hard because a previously visited location affects the decision for the future locations. One key component of our work is path planning by building a sparse graph that captures these dependencies. The other key component is our learning algorithm that weighs the errors made in robot's data collection process while updating the classifier. In our experiments, we show that our algorithms enable the robot to improve its object classifiers significantly.