Ing. Tomáš Petříček successfully defended his Ph.D. thesis entitled Coupled Learning and Planning for Active 3D Mapping (supervisor: doc. Ing. Tomáš Svoboda, Ph.D.). Congratulations!
Four papers, one as oral presentation, were accepted at ICCV2017 which is one of the most prestigious computer vision conferences with a very selective review process and low acceptance rate. Congratulations to Zuzana Kukelova, Jiri Matas, Michal Busta, Lukas Neumann, Karel Zimmermann, Tomas Petricek, Vojtech Salansky, and Tomas Svoboda.
Paper of authors K. Zimmermann, T. Petricek, V. Salansky, and T. Svoboda was accepted as oral presentation at ICCV 2017! We propose an active 3D mapping method for depth sensors, which allow individual control of depth-measuring rays, such as the newly emerging solid-state lidars. The method simultaneously (i) learns to reconstruct a dense 3D occupancy …read more
Assoc. Prof. Tomáš Svoboda (Vision for Robotics and Autonomous Systems) was a guest on CT24 speaking on the topic: USA: Shared Autonomous Transport in 13 Years? (the interview in Czech language only starts at about 0:15:56).
A rescue ground robot is going to Italy after earthquake. Real deployment event featured in Czech TV main news. Department is collaborating with CIIRC within TRADR project.
Enable-S3 is a large ECSEL-JU project called European Initiative to Enable Validation for Highly Automated Safe and Secure Systems. Tomas Svoboda is the PI of the CTU part. We will closely collaborate with ValeoCZ working on boosting rare data into the learning and testing in automatic driving.
Tomáš Svoboda discussed on DVTV with Martin Veselovský the future of transportation systems and autonomous vehicles (in Czech). Many thanks for successful representation of our department!
Our colleague, Assoc. Prof. Tomáš Svoboda (Center for Machine Perception, Center for Robotics and Autonomous Systems, was invited to the Magazine Leonardo (in Czech) of the Czech Radio Plus channel. He discussed (in Czech) about a rescue robot called TRADR. Many thanks for the representation of our department!
The successively evaluated features used in a sliding window detection process to decide about object presence/absence also contain knowledge about object deformation. We exploit these detection features to estimate the object deformation. Estimated deformation is then immediately applied to not yet evaluated features to align them with the observed image data. In our approach, the …read more
We design and evaluate a data fusion system for localization of a mobile skid-steer robot intended for USAR missions. We exploit a rich sensor suite including both proprioceptive (inertial measurement unit and tracks odometry) and exteroceptive sensors (omnidirectional camera and rotating laser rangefinder). To cope with the specificities of each sensing modality (such as significantly …read more