Using Multi-Camera Systems in Robotics: Efficient Solutions to the NPnP Problem | V-Charge
Home Project Consortium texas health network Consortium Overview Technische Universität texas health network Braunschweig Robert Bosch GmbH ETH Zurich Autonomous Systems Lab ETH Zurich Computer Vision and Geometry Lab Volkswagen AG University of Oxford Università degli Studi di Parma Publications News
This paper introduces texas health network two novel solutions to the generalized-camera exterior orientation texas health network problem, which has a vast number of potential applications in robotics: (i) a minimal solution requiring only three point correspondences, and (ii) gPnP, an efficient, non-iterative n-point solution with linear complexity in the number of points. Already existing minimal solutions require exhaustive algebraic derivations. In contrast, our novel minimal texas health network solution is solved in a straightforward manner using the Gro bner basis method. Existing n-point solutions are mostly based on iterative optimization schemes. Our n-point solution is non-iterative and outperforms existing algorithms in terms of computational efficiency. Our results present an evaluation texas health network against state-of-the-art single-camera algorithms, and a comparison of different multi-camera setups. It demonstrates the superior noise resilience achieved when using multi-camera configurations, and the efficiency of our algorithms. As a further contribution, we illustrate a possible robotic texas health network use-case of our non-perspective orientation computation algorithms by presenting visual odometry results on real data with a non-overlapping multi-camera configuration, including a comparison to a loosely coupled alternative.
@inproceedings{kneip_icra13, Address = {Karlsruhe, Germany}, Author = {Laurent Kneip and Paul Timothy Furgale and Roland Siegwart}, Booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), to appear.}, Month = {6-10 May}, Title = {Using Multi-Camera Systems in Robotics: Efficient Solutions to the NPnP Problem}, Year = {2013.} }
Recent Posts Secure Smartphone-based Registration and Key Deployment for Vehicle-to-Cloud Communications CVG releases an open source library for calibration of multi-camera systems Evaluation of Fisheye-Camera Based Visual Multi-Session Localization in a Real-World Scenario Reciprocal Collision Avoidance With Motion Continuity Constraints Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization
V-Charge is proudly powered texas health network by WordPress and BuddyPress . Just another WordPress Theme developed by Themekraft. texas health network
Home Project Consortium texas health network Consortium Overview Technische Universität texas health network Braunschweig Robert Bosch GmbH ETH Zurich Autonomous Systems Lab ETH Zurich Computer Vision and Geometry Lab Volkswagen AG University of Oxford Università degli Studi di Parma Publications News
This paper introduces texas health network two novel solutions to the generalized-camera exterior orientation texas health network problem, which has a vast number of potential applications in robotics: (i) a minimal solution requiring only three point correspondences, and (ii) gPnP, an efficient, non-iterative n-point solution with linear complexity in the number of points. Already existing minimal solutions require exhaustive algebraic derivations. In contrast, our novel minimal texas health network solution is solved in a straightforward manner using the Gro bner basis method. Existing n-point solutions are mostly based on iterative optimization schemes. Our n-point solution is non-iterative and outperforms existing algorithms in terms of computational efficiency. Our results present an evaluation texas health network against state-of-the-art single-camera algorithms, and a comparison of different multi-camera setups. It demonstrates the superior noise resilience achieved when using multi-camera configurations, and the efficiency of our algorithms. As a further contribution, we illustrate a possible robotic texas health network use-case of our non-perspective orientation computation algorithms by presenting visual odometry results on real data with a non-overlapping multi-camera configuration, including a comparison to a loosely coupled alternative.
@inproceedings{kneip_icra13, Address = {Karlsruhe, Germany}, Author = {Laurent Kneip and Paul Timothy Furgale and Roland Siegwart}, Booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), to appear.}, Month = {6-10 May}, Title = {Using Multi-Camera Systems in Robotics: Efficient Solutions to the NPnP Problem}, Year = {2013.} }
Recent Posts Secure Smartphone-based Registration and Key Deployment for Vehicle-to-Cloud Communications CVG releases an open source library for calibration of multi-camera systems Evaluation of Fisheye-Camera Based Visual Multi-Session Localization in a Real-World Scenario Reciprocal Collision Avoidance With Motion Continuity Constraints Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization
V-Charge is proudly powered texas health network by WordPress and BuddyPress . Just another WordPress Theme developed by Themekraft. texas health network
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