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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Visual-Inertial Object Detection and Mapping

verfasst von : Xiaohan Fei, Stefano Soatto

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

We present a method to populate an unknown environment with models of previously seen objects, placed in a Euclidean reference frame that is inferred causally and on-line using monocular video along with inertial sensors. The system we implement returns a sparse point cloud for the regions of the scene that are visible but not recognized as a previously seen object, and a detailed object model and its pose in the Euclidean frame otherwise. The system includes bottom-up and top-down components, whereby deep networks trained for detection provide likelihood scores for object hypotheses provided by a nonlinear filter, whose state serves as memory. Additional networks provide likelihood scores for edges, which complements detection networks trained to be invariant to small deformations. We test our algorithm on existing datasets, and also introduce the VISMA dataset, that provides ground truth pose, point-cloud map, and object models, along with time-stamped inertial measurements.

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Vorheriges Kapitel Integrating Egocentric Videos in Top-View Surveillance Videos: Joint Identification and Temporal Alignment
Nächstes Kapitel Actor-Centric Relation Network
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Literatur
1.
Salas-Moreno, R.F., Newcombe, R.A., Strasdat, H., Kelly, P.H., Davison, A.J.: SLAM++: simultaneous localisation and mapping at the level of objects. In: Computer Vision and Pattern Recognition (CVPR) (2013)
2.
Dong, J., Fei, X., Soatto, S.: Visual-inertial-semantic scene representation for 3D object detection. In: Computer Vision and Pattern Recognition (CVPR) (2017)
3.
Jazwinski, A.: Stochastic Processes and Filtering Theory. Academic Press, Cambridge (1970)
4.
Mourikis, A., Roumeliotis, S.: A multi-state constraint kalman filter for vision-aided inertial navigation. In: International Conference on Robotics and Automation (ICRA) (2007)
5.
Tsotsos, K., Chiuso, A., Soatto, S.: Robust inference for visual-inertial sensor fusion. In: International Conference on Robotics and Automation (ICRA) (2015)
6.
Civera, J., Davison, A.J., Montiel, J.M.: Inverse depth parametrization for monocular slam. IEEE Trans. Robot. 24(5), 932–945 (2008)CrossRef
7.
Blake, A., Isard, M.: The condensation algorithm-conditional density propagation and applications to visual tracking. In: Advances in Neural Information Processing Systems (NIPS) (1997)
8.
Drummond, T., Cipolla, R.: Real-time visual tracking of complex structures. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 24(7), 932–946 (2002)CrossRef
9.
Klein, G., Murray, D.W.: Full-3D edge tracking with a particle filter. In: British Machine Vision Conference (BMVC) (2006)
10.
Canny, J.: A computational approach to edge detection. In: Readings in Computer Vision, pp. 184–203. Elsevier (1987)
11.
Gordon, N.J., Salmond, D.J., Smith, A.F.: Novel approach to nonlinear/non-gaussian bayesian state estimation. In: IEE Proceedings F (Radar and Signal Processing), vol. 140, pp. 107–113. IET (1993)
12.
13.
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 12, 2481–2495 (2017)CrossRef
14.
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: International Conference on Computer Vision (ICCV) (2001)
15.
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D slam systems. In: International Conference on Intelligent Robots and Systems (IROS) (2012)
16.
Handa, A., Whelan, T., McDonald, J., Davison, A.J.: A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. In: International Conference on Robotics and Automation (ICRA) (2014)
17.
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. (IJRR) 32(11), 1231–1237 (2013)CrossRef
18.
Burri, M.: The EuRoC micro aerial vehicle datasets. Int. J. Robot. Res. (IJCV) 35(10), 1157–1163 (2016)CrossRef
19.
Pfrommer, B., Sanket, N., Daniilidis, K., Cleveland, J.: Penncosyvio: a challenging visual inertial odometry benchmark. In: International Conference on Robotics and Automation (ICRA) (2017)
20.
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. (IJCV) 88(2), 303–338 (2010)CrossRef
21.
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)MathSciNetCrossRef
22.
23.
Xiang, Y., Mottaghi, R., Savarese, S.: Beyond pascal: a benchmark for 3D object detection in the wild. In: Winter Conference on Applications of Computer Vision (WACV) (2014)
24.
25.
Hua, B.S., Pham, Q.H., Nguyen, D.T., Tran, M.K., Yu, L.F., Yeung, S.K.: Scenenn: a scene meshes dataset with annotations. In: 3D Vision (3DV) (2016)
26.
Savva, M., et al.: Shrec16 track large-scale 3D shape retrieval from shapenet core55. In: Proceedings of the Eurographics Workshop on 3D Object Retrieval (2016)
27.
Whelan, T., Leutenegger, S., Salas-Moreno, R.F., Glocker, B., Davison, A.J.: Elasticfusion: dense slam without a pose graph. In: Robotics: Science and Systems (RSS) (2015)
28.
29.
Castle, R.O., Klein, G., Murray, D.W.: Combining monoslam with object recognition for scene augmentation using a wearable camera. Image Vis. Comput. 28(11), 1548–1556 (2010)CrossRef
30.
Civera, J., Gálvez-López, D., Riazuelo, L., Tardós, J.D., Montiel, J.: Towards semantic slam using a monocular camera. In: International Conference on Intelligent Robots and Systems (IROS) (2011)
31.
Kundu, A., Li, Y., Dellaert, F., Li, F., Rehg, J.: Joint semantic segmentation and 3D reconstruction from monocular video. In: European Conference Computer Vision (ECCV) (2014)
32.
Hermans, A., Floros, G., Leibe, B.: Dense 3D semantic mapping of indoor scenes from RGB-D images. In: International Conference on Robotics and Automation (ICRA) (2014)
33.
Vineet, V.E.A.: Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction. In: International Conference on Robotics and Automation (ICRA) (2015)
34.
McCormac, J., Handa, A., Davison, A., Leutenegger, S.: Semanticfusion: dense 3D semantic mapping with convolutional neural networks. In: International Conference on Robotics and Automation (ICRA) (2017)
35.
Bowman, S.L., Atanasov, N., Daniilidis, K., Pappas, G.J.: Probabilistic data association for semantic slam. In: International Conference on Robotics and Automation (ICRA) (2017)
36.
Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: International Symposium on Mixed and Augmented Reality (ISMAR) (2007)
37.
Girshick, R.: Fast R-CNN. In: International Conference on Computer Vision (ICCV) (2015)
38.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NIPS) (2015)
39.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: International Conference on Computer Vision (ICCV) (2017)
40.
41.
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Computer Vision and Pattern Recognition (CVPR) (2016)
42.
Choi, C., Christensen, H.I.: 3D textureless object detection and tracking: An edge-based approach. In: International Conference on Intelligent Robots and Systems (IROS) (2012)
43.
Lepetit, V., Fua, P., et al.: Monocular model-based 3D tracking of rigid objects: a survey. In: Foundations and Trends\({\textregistered }\) in Computer Graphics and Vision (2005)CrossRef
44.
Prisacariu, V.A., Reid, I.D.: Pwp3D: real-time segmentation and tracking of 3D objects. Int. J. Comput. Vis. (IJCV) 98(3), 335–354 (2012)MathSciNetCrossRef
45.
Tjaden, H., Schwanecke, U., Schömer, E.: Real-time monocular pose estimation of 3D objects using temporally consistent local color histograms. In: Computer Vision and Pattern Recognition (CVPR) (2017)
Metadaten
Titel
Visual-Inertial Object Detection and Mapping
verfasst von
Xiaohan Fei
Stefano Soatto
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01251-9

Electronic ISBN: 978-3-030-01252-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01252-6_19

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