Robust Detection of People in Thermal Imagery
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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
Detecting Humans in 2D Thermal Images by Generating 3D Models
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Sliding-Windows for Rapid Object Class Localization: A Parallel Technique
Proceedings of the 30th DAGM symposium on Pattern Recognition
Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Semantic place classification of indoor environments with mobile robots using boosting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Building a world model for a mobile robot using dynamic semantic constraints
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Centralized fusion for fast people detection in dense environment
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Spatial correlation of multi-sensor features for autonomous victim identification
Robot Soccer World Cup XV
Hi-index | 0.00 |
In urban search and rescue scenarios, typical applications of robots include autonomous exploration of possibly dangerous sites, and the recognition of victims and other objects of interest. In complex scenarios, relying on only one type of sensor is often misleading, while using complementary sensors frequently helps improving the performance. To that end, we propose a probabilistic world model that leverages information from heterogeneous sensors and integrates semantic attributes. This method of reasoning about complementary information is shown to be advantageous, yielding increased reliability compared to considering all sensors separately. We report results from several experiments with a wheeled USAR robot in a complex indoor scenario. The robot is able to learn an accurate map, and to detect real persons and signs of hazardous materials based on inertial sensing, odometry, a laser range finder, visual detection, and thermal imaging. The results show that combining heterogeneous sensor information increases the detection performance, and that semantic attributes can be successfully integrated into the world model.