publication . Preprint . Conference object . 2017

Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection

Edgar Sucar; Jean-Bernard Hayet;
Open Access English
  • Published: 27 May 2017
Abstract
This paper proposes a novel method to estimate the global scale of a 3D reconstructed model within a Kalman filtering-based monocular SLAM algorithm. Our Bayesian framework integrates height priors over the detected objects belonging to a set of broad predefined classes, based on recent advances in fast generic object detection. Each observation is produced on single frames, so that we do not need a data association process along video frames. This is because we associate the height priors with the image region sizes at image places where map features projections fall within the object detection regions. We present very promising results of this approach obtaine...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Probabilistic logic, Computer science, Kalman filter, Artificial intelligence, business.industry, business, Scale estimation, Pattern recognition, Bayesian probability, Object detection, Computer vision, Prior probability, Detector, Simultaneous localization and mapping
Related Organizations
18 references, page 1 of 2

[1] J. Civera, A. J. Davison, and J. M. M. Montiel. Dimensionless monocular slam. In Proc. of the Iberian Conf. on Pattern Recognition and Image Analysis, 2007. 3

[2] A. J. Davison. Real-Time Simultaneous Localisation and Mapping with a Single Camera. In Int. Conf. Comput. Vis., 2003. 1, 2, 3, 4, 5

[3] M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes challenge: A retrospective. Int. Journal of Computer Vision, 111(1):98-136, 2015. 5 [OpenAIRE]

[4] D. P. Frost, O. Ka¨hler, and D. W. Murray. Object-aware bundle adjustment for correcting monocular scale drift. In Proc. of Int. Conf. on Robotics and Automation, pages 4770-4776, May 2016. 2

[5] D. Ga´lvez-Lo´ pez, M. Salas, J. D. Tardo´ s, and J. Montiel. Real-time monocular object slam. Robotics and Autonomous Systems, 75, Part B:435 - 449, 2016. 1, 2

[6] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. Vision meets robotics: The kitti dataset. Int. Journal of Robotics Research (IJRR), 2013. 8

[7] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proc. of Int. Conf. on Computer Vision and Pattern Recognition, 2014. 3

[8] G. Klein and D. Murray. Parallel Tracking and Mapping for Small AR Workspaces. In Proc. of Int. Symp. Mix. Augment. Real. IEEE, Nov. 2007. 1, 2

[9] S. B. Knorr and D. Kurz. Leveraging the user's face for absolute scale estimation in handheld monocular slam. Proc. of Int. Symp. on Mixed and Augmented Reality, 00:11-17, 2016. 2

[10] S. A. Mota-Gutierrez, J.-B. Hayet, S. Ruiz-Correa, R. Hasimoto, and C. E. Zubieta-Rico. Learning depth from appearance for fast one-shot 3-D map initialization in VSLAM systems. In Proc. of Int. Conf. Robot. Autom., 2013. 1

[11] R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohli, J. Shotton, S. Hodges, and A. a. Fitzgibbon. Kinectfusion: Real-time dense surface mapping and tracking. In Proc. of Int. Symp. on Mixed and Augmented Reality. IEEE, October 2011. 1, 2

[12] G. N u¨tzi, S. Weiss, D. Scaramuzza, and R. Siegwart. Fusion of imu and vision for absolute scale estimation in monocular slam. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011. 1, 2

[13] J. Redmon and A. Farhadi. Yolo9000: Better, faster, stronger. arXiv preprint arXiv:1612.08242, 2016. 3, 5

[14] R. Salas-Moreno, R. Newcombe, H. Strasdat, P. Kelly, and A. Davison. Slam++: Simultaneous localisation and mapping at the level of objects. In Proc. of Int. Conf. on Computer Vision and Pattern Recognition, 2013. 1, 2 [OpenAIRE]

[15] A. Saxena, M. Sun, and A. Y. Ng. Make3D: Learning 3D Scene Structure from a Single Still Image. IEEE Trans. Pattern Anal. Mach. Intell., 31(5), May 2009. 1 [OpenAIRE]

18 references, page 1 of 2
Abstract
This paper proposes a novel method to estimate the global scale of a 3D reconstructed model within a Kalman filtering-based monocular SLAM algorithm. Our Bayesian framework integrates height priors over the detected objects belonging to a set of broad predefined classes, based on recent advances in fast generic object detection. Each observation is produced on single frames, so that we do not need a data association process along video frames. This is because we associate the height priors with the image region sizes at image places where map features projections fall within the object detection regions. We present very promising results of this approach obtaine...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Probabilistic logic, Computer science, Kalman filter, Artificial intelligence, business.industry, business, Scale estimation, Pattern recognition, Bayesian probability, Object detection, Computer vision, Prior probability, Detector, Simultaneous localization and mapping
Related Organizations
18 references, page 1 of 2

[1] J. Civera, A. J. Davison, and J. M. M. Montiel. Dimensionless monocular slam. In Proc. of the Iberian Conf. on Pattern Recognition and Image Analysis, 2007. 3

[2] A. J. Davison. Real-Time Simultaneous Localisation and Mapping with a Single Camera. In Int. Conf. Comput. Vis., 2003. 1, 2, 3, 4, 5

[3] M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes challenge: A retrospective. Int. Journal of Computer Vision, 111(1):98-136, 2015. 5 [OpenAIRE]

[4] D. P. Frost, O. Ka¨hler, and D. W. Murray. Object-aware bundle adjustment for correcting monocular scale drift. In Proc. of Int. Conf. on Robotics and Automation, pages 4770-4776, May 2016. 2

[5] D. Ga´lvez-Lo´ pez, M. Salas, J. D. Tardo´ s, and J. Montiel. Real-time monocular object slam. Robotics and Autonomous Systems, 75, Part B:435 - 449, 2016. 1, 2

[6] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. Vision meets robotics: The kitti dataset. Int. Journal of Robotics Research (IJRR), 2013. 8

[7] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proc. of Int. Conf. on Computer Vision and Pattern Recognition, 2014. 3

[8] G. Klein and D. Murray. Parallel Tracking and Mapping for Small AR Workspaces. In Proc. of Int. Symp. Mix. Augment. Real. IEEE, Nov. 2007. 1, 2

[9] S. B. Knorr and D. Kurz. Leveraging the user's face for absolute scale estimation in handheld monocular slam. Proc. of Int. Symp. on Mixed and Augmented Reality, 00:11-17, 2016. 2

[10] S. A. Mota-Gutierrez, J.-B. Hayet, S. Ruiz-Correa, R. Hasimoto, and C. E. Zubieta-Rico. Learning depth from appearance for fast one-shot 3-D map initialization in VSLAM systems. In Proc. of Int. Conf. Robot. Autom., 2013. 1

[11] R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohli, J. Shotton, S. Hodges, and A. a. Fitzgibbon. Kinectfusion: Real-time dense surface mapping and tracking. In Proc. of Int. Symp. on Mixed and Augmented Reality. IEEE, October 2011. 1, 2

[12] G. N u¨tzi, S. Weiss, D. Scaramuzza, and R. Siegwart. Fusion of imu and vision for absolute scale estimation in monocular slam. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011. 1, 2

[13] J. Redmon and A. Farhadi. Yolo9000: Better, faster, stronger. arXiv preprint arXiv:1612.08242, 2016. 3, 5

[14] R. Salas-Moreno, R. Newcombe, H. Strasdat, P. Kelly, and A. Davison. Slam++: Simultaneous localisation and mapping at the level of objects. In Proc. of Int. Conf. on Computer Vision and Pattern Recognition, 2013. 1, 2 [OpenAIRE]

[15] A. Saxena, M. Sun, and A. Y. Ng. Make3D: Learning 3D Scene Structure from a Single Still Image. IEEE Trans. Pattern Anal. Mach. Intell., 31(5), May 2009. 1 [OpenAIRE]

18 references, page 1 of 2
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