Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (01): 56-60,76.doi: 10.12052/gdutxb.210028

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Visual Inertial Odometry Based on Deep Features

Xu Wei-feng, Cai Shu-ting, Xiong Xiao-ming   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2021-02-18 Online:2023-01-25 Published:2023-01-12

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Abstract: Visual odometry is the cornerstone in the field of SLAM. Monocular visual odometry occupies an important position because of its low cost and less camera calibration, but it has some shortcomings such as scale uncertainty, scale drift, poor robustness, and so on. To solve these problems, based on ORB-SLAM3, we process a monocular visual-inertial navigation odometer with depth features, referred to as DF-VIO (Visual Inertial Odometry Based on Deep Features) , which uses depth features extracted by deep learning network to replace traditional artificial point features, and fuses artificial line features to enhance the robustness of the system in real complex scenes. Besides, the system provides a variety of pose tracking methods. In addition to the method based on the constant speed model and tracking reference keyframe, a pose tracking method based on the predicted repeatability map is also provided, which further improves the accuracy of system pose tracking. Comparative experiments are carried out on the open data set EuRoC, and the average trajectory error is reduced by 25.9% in pure visual mode and 8.6% in visual-inertial mode, which proves that the system proposed in this paper can be more robust in complex scenes.

Key words: visual inertial odometry, deep learning, inertial measurement unit, line features

CLC Number: 

  • TP301
[1] CADENA C, CARLONE L, CARRILLO H, et al. Past, present, and future of simultaneous localization and mapping: toward the robust-perception age [J]. IEEE Transactions on Robotics, 2016, 32(6): 1309-1332.
[2] PARK S, SCHÖPS T, POLLEFEYS M. Illumination change robustness in direct visual slam[C]//2017 IEEE international conference on Robotics and Automation (ICRA) . Singapore: IEEE, 2017: 4523-4530.
[3] 池鹏可, 苏成悦. 移动机器人中单目视觉里程计的研究[J]. 广东工业大学学报, 2017, 34(5): 40-44.
CHI P K, SU C Y. A Research on monocular visual odometry for mobile robots [J]. Journal of Guangdong University of Technology, 2017, 34(5): 40-44.
[4] 汝少楠, 何元烈, 叶星余. 基于稀疏直接法闭环检测定位的视觉里程计[J]. 广东工业大学学报, 2021, 38(3): 48-54.
RU S N, HE Y L, YE X Y. Visual odometry based on sparse direct method loop-closure detection [J]. Journal of Guangdong University of Technology, 2021, 38(3): 48-54.
[5] STRASDAT H, MONTIEL J, DAVISON A J. Scale drift-aware large scale monocular SLAM[C]// Robotics: Science and Systems VI. Zaragoza: MIT Press, 2010.
[6] MUR-ARTAL R, TARDÓS J D. Visual-inertial monocular SLAM with map reuse [J]. IEEE Robotics and Automation Letters, 2017, 2(2): 796-803.
[7] FORSTER C, CARLONE L, DELLAERT F, et al. On-manifold preintegration for real-time visual-inertial odometry [J]. IEEE Transactions on Robotics, 2016, 33(1): 1-21.
[8] QIN T, SHEN S. Online temporal calibration for monocular visual-inertial systems[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Madrid: IEEE, 2018: 3662-3669.
[9] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: Optimal speed and accuracy of object detection[EB/OL]. arXiv: 2004.10934 (2020-04-23). https://arxiv.org/abs/2004.10934.
[10] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 3431-3440.
[11] EIGEN D, FERGUS R. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture[C]//Proceedings of the IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 2650-2658.
[12] TORRES-CAMARA J M, ESCALONA F, GOMEZ-DONOSO F, et al. Map slammer: densifying scattered KSLAM 3D maps with estimated depth[C]//Iberian Robotics Conference. Porto: Springer, Cham, 2019: 563-574.
[13] YANG S, SCHERER S. Cubeslam: monocular 3-D object SLAM [J]. IEEE Transactions on Robotics, 2019, 35(4): 925-938.
[14] GRINVALD M, FURRER F, NOVKOVIC T, et al. Volumetric instance-aware semantic mapping and 3D object discovery [J]. IEEE Robotics and Automation Letters, 2019, 4(3): 3037-3044.
[15] DETONE D, MALISIEWICZ T, RABINOVICH A. Superpoint: self-supervised interest point detection and description[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 224-236.
[16] GROMPONE VON GIOI R, JAKUBOWICZ J, MOREL J M, et al. LSD: a fast line segment detector with a false detection control [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 32(4): 722-732.
[17] HUANG H, YE H, SUN Y, et al. Monocular visual odometry using learned repeatability and description[C]//2020 IEEE International Conference on Robotics and Automation (ICRA) . Paris: IEEE, 2020: 8913-8919.
[18] CAMPOS C, ELVIRA R, RODRÍGUEZ J J G, et al. ORB-SLAM3: an accurate open-source library for visual, visual-inertial and multi-map SLAM[EB/OL]. arXiv: 2007.11898 (2020-07-23). https://arxiv.org/abs/2007.1189801.
[19] BURRI M, NIKOLIC J, GOHL P, et al. The EuRoC micro aerial vehicle datasets [J]. The International Journal of Robotics Research, 2016, 35(10): 1157-1163.
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