Bi-direction Direct RGB-D Visual Odometry

References

• Aguilar, W. G., G. A. Rodrguez, L. Álvarez, S. Sandoval, F. Quisaguano, and A. Limaico. 2017a. On-board visual SLAM on a UGV using a RGB-D camera. In International Conference on Intelligent Robotics and Applications, 298–308, Springer, Wuhan, China.
   Google Scholar
• Aguilar, W. G., G. A. Rodrguez, L. Álvarez, S. Sandoval, F. Quisaguano, and A. Limaico. 2017b. Real-time 3D modeling with a RGB-D camera and on-board processing. In International Conference on Augmented Reality, Virtual Reality and Computer Graphics, 410–19, Springer, Ugento, Italy.
   Google Scholar
• Alismail, H., B. Browning, and S. Lucey. 2016. Enhancing direct camera tracking with dense feature descriptors. In Asian Conference on Computer Vision, 535–51, Springer, Taipei, Taiwan.
   Google Scholar
• Babu, B. W., S. Kim, Z. Yan, and L. Ren. 2016. σ-dvo: Sensor noise model meets dense visual odometry. In 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 18–26, IEEE, Merida, Yucatan, Mexico.
   Google Scholar
• Baker, S., and I. Matthews. 2004. Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision 56 (3):221–55. doi:10.1023/B:VISI.0000011205.11775.fd.
   Web of Science ®Google Scholar
• Bergmann, P., R. Wang, and D. Cremers. 2017. Online photometric calibration of auto exposure video for realtime visual odometry and slam. IEEE Robotics and Automation Letters 3 (2):627–34. doi:10.1109/LRA.2017.2777002.
   Web of Science ®Google Scholar
• Christensen, K., and M. Hebert. 2019. Edge-direct visual odometry. arXiv preprint arXiv:1906.04838.
   Google Scholar
• Civera, J., and S. H. Lee. 2019. RGB-D Image Analysis and Processing. In Advances in Computer Vision and Pattern Recognition, edited by Rosin, P.L., Lai, Y.-K., Shao, L. and Liu, Y., 117-44. Cham: Springer.
   Google Scholar
• Cvišic, I., J. Cesic, I. Markovic, and I. Petrovic. 2017. Soft-slam: Computationally efficient stereo visual slam for autonomous uavs. Journal of Field Robotics 35 (4):578–595. doi:10.1002/rob.21762.
   Google Scholar
• Deigmoeller, J., and J. Eggert. 2016. Stereo visual odometry without temporal filtering. In German Conference on Pattern Recognition, 166–75, Springer, Hannover, Germany.
   Google Scholar
• Dos Reis, D. H., D. Welfer, M. A. De Souza Leite Cuadros, and D. F. T. Gamarra. 2019. Mobile robot navigation using an object recognition software with RGBD images and the YOLO algorithm. Applied Artificial Intelligence 33 (14):1290–305. doi:10.1080/08839514.2019.1684778.
   Web of Science ®Google Scholar
• Endres, F., J. Hess, J. Sturm, D. Cremers, and W. Burgard. 2013. 3-D mapping with an RGB-D camera. IEEE Transactions on Robotics 30 (1):177–87. doi:10.1109/TRO.2013.2279412.
   Web of Science ®Google Scholar
• Engel, J., V. Koltun, and D. Cremers. 2017. Direct sparse odometry. IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (3):611–25. doi:10.1109/TPAMI.2017.2658577.
   PubMed Web of Science ®Google Scholar
• Engel, J., V. Usenko, and D. Cremers. 2016. A photometrically calibrated benchmark for monocular visual odometry. arXiv preprint arXiv:1607.02555.
   Google Scholar
• Handa, A., T. Whelan, J. McDonald, and A. J. Davison. 2014. A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. In 2014 IEEE international conference on Robotics and automation (ICRA), 1524–31, IEEE, Hong Kong, China.
   Google Scholar
• Hu, Y., R. Song, and Y. Li. 2016. Efficient coarse-to-fine patchmatch for large displacement optical flow. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5704–12, IEEE, Las Vegas, NV, USA.
   Google Scholar
• Iacono, M., and A. Sgorbissa. 2018. Path following and obstacle avoidance for an autonomous UAV using a depth camera. Robotics and Autonomous Systems 106:38–46. doi:10.1016/j.robot.2018.04.005.
   Web of Science ®Google Scholar
• Kerl, C., J. Sturm, and D. Cremers. 2013. Robust odometry estimation for RGB-D cameras. In 2013 IEEE International Conference on Robotics and Automation, 3748–54, IEEE, Karlsruhe, Germany.
   Google Scholar
• Klose, S., P. Heise, and A. Knoll. 2013. Efficient compositional approaches for real-time robust direct visual odometry from RGB-D data. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1100–06, IEEE, Tokyo, Japan.
   Google Scholar
• Kuse, M., and S. Shen. 2016. Robust camera motion estimation using direct edge alignment and sub-gradient method. In 2016 IEEE International Conference on Robotics and Automation (ICRA), 573–79, IEEE, Stockholm, Sweden.
   Google Scholar
• Lin, Y., F. Gao, T. Qin, W. Gao, T. Liu, W. Wu, Z. Yang, and S. Shen. 2018. Autonomous aerial navigation using monocular visual-inertial fusion. Journal of Field Robotics 35 (1):23–51. doi:10.1002/rob.21732.
   Web of Science ®Google Scholar
• Ling, Y., and S. Shen. 2019. Real-time dense mapping for online processing and navigation. Journal of Field Robotics 36 (5):1004–36. doi:10.1002/rob.21868.
   Web of Science ®Google Scholar
• Markley, F. L., Y. Cheng, J. L. Crassidis, and Y. Oshman. 2007. Averaging quaternions. Journal of Guidance, Control, and Dynamics 30 (4):1193–97. doi:10.2514/1.28949.
   Web of Science ®Google Scholar
• Moore, H. 2017. MATLAB for engineers. Upper Saddle River, New Jersey: Pearson.
   Google Scholar
• Mur-Artal, R., and J. D. Tardós. 2017. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Transactions on Robotics 33 (5):1255–62. doi:10.1109/TRO.2017.2705103.
   Web of Science ®Google Scholar
• Park, S., T. Schöps, and M. Pollefeys. 2017. Illumination change robustness in direct visual slam. In 2017 IEEE international conference on robotics and automation (ICRA), 4523–30, IEEE, Singapore, Singapore.
   Google Scholar
• Pereira, F., J. Luft, G. Ilha, A. Sofiatti, and A. Susin. 2017. Backward motion for estimation enhancement in sparse visual odometry. In 2017 Workshop of Computer Vision (WVC), 61–66, IEEE, Natal, Brazil.
   Google Scholar
• Pillai, S., and J. J. Leonard. 2017. Towards visual ego-motion learning in robots. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5533–40, IEEE, Vancouver, BC, Canada.
   Google Scholar
• Proesmans, M., L. Van Gool, E. Pauwels, and A. Oosterlinck. 1994. Determination of optical flow and its discontinuities using non-linear diffusion. In European Conference on Computer Vision, 294–304, Springer, Stockholm, Sweden.
   Google Scholar
• Revaud, J., P. Weinzaepfel, Z. Harchaoui, and C. Schmid. 2015. Epicflow: Edge-preserving interpolation of correspondences for optical flow. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1164–72, IEEE, Boston, MA, USA.
   Google Scholar
• Schenk, F., and F. Fraundorfer. 2017a. Combining edge images and depth maps for robust visual odometry. In British Machine Vision Conference (BMVC), BMVA Press, London, UK.
   Google Scholar
• Schenk, F., and F. Fraundorfer. 2017b. Robust edge-based visual odometry using machine-learned edges. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1297–304, IEEE, Vancouver, BC, Canada.
   Google Scholar
• Schenk, F., and F. Fraundorfer. 2019. RESLAM: A real-time robust edge-based SLAM system. In 2019 International Conference on Robotics and Automation (ICRA), 154–60, IEEE, Montreal, QC, Canada.
   Google Scholar
• Schops, T., T. Sattler, and M. Pollefeys. 2019. BAD SLAM: Bundle adjusted direct RGB-D SLAM. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 134–44, IEEE, Long Beach, CA, USA.
   Google Scholar
• Sturm, J., N. Engelhard, F. Endres, W. Burgard, and D. Cremers. 2012. A benchmark for the evaluation of RGB-D SLAM systems. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 573–80. IEEE, Vilamoura, Algarve, Portugal.
   Google Scholar
• Wan, Y., W. Gao, and Y. Wu. 2019. Optical flow assisted monocular visual odometry. In Asian Conference on Pattern Recognition, 366–77, Springer, Auckland, New Zealand.
   Google Scholar
• Wasenmüller, O., M. D. Ansari, and D. Stricker. 2016. Dna-slam: Dense noise aware slam for tof rgb-d cameras. In Asian Conference on Computer Vision Workshop, 613–29, Springer, Taipei, Taiwan.
   Google Scholar
• Wong, A., X. Fei, S. Tsuei, and S. Soatto. 2020. Unsupervised depth completion from visual inertial odometry. IEEE Robotics and Automation Letters 5 (2):1899–906. doi:10.1109/LRA.2020.2969938.
   Web of Science ®Google Scholar
• Yang, N., R. Wang, X. Gao, and D. Cremers. 2018. Challenges in monocular visual odometry: Photometric calibration, motion bias, and rolling shutter effect. IEEE Robotics and Automation Letters 3 (4):2878–85. doi:10.1109/LRA.2018.2846813.
   Web of Science ®Google Scholar
• Yin, Z., and J. Shi. 2018. Geonet: Unsupervised learning of dense depth, optical flow and camera pose. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1983–92, IEEE, Salt Lake City, UT, USA.
   Google Scholar
• Zhou, T., P. Krahenbuhl, M. Aubry, Q. Huang, and A. A. Efros. 2016. Learning dense correspondence via 3d-guided cycle consistency. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 117–26, IEEE, Las Vegas, NV, USA.
   Google Scholar
• Zhou, Y., H. Li, and L. Kneip. 2018. Canny-vo: Visual odometry with rgb-d cameras based on geometric 3-d–2-d edge alignment. IEEE Transactions on Robotics 35 (1):184–99. doi:10.1109/TRO.2018.2875382.
   Web of Science ®Google Scholar
• Zubizarreta, J., I. Aguinaga, and J. M. M. Montiel. 2020. Direct sparse mapping. IEEE Transactions on Robotics 36 (4):1363–70. doi:10.1109/TRO.2020.2991614.
   Web of Science ®Google Scholar