publication . Article . Other literature type . 2019


Zachi Shtain; Sagi Filin;
Open Access
  • Published: 29 Nov 2019 Journal: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, volume XLII-2/W17, pages 323-329 (eissn: 2194-9034, Copyright policy)
  • Publisher: Copernicus GmbH
While lightweight stereo vision sensors provide detailed and high-resolution information that allows robust and accurate localization, the computation demands required for such process is doubled compared to monocular sensors. In this paper, an alternative model for pose estimation of stereo sensors is introduced which provides an efficient and precise framework for investigating system configurations and maximize pose accuracies. Using the proposed formulation, we examine the parameters that affect accurate pose estimation and their magnitudes and show that for standard operational altitudes of ∼50 m, a five-fold improvement in localization is reached, from ∼0.4–0.5 m with a single sensor to less than 0.1 m by taking advantage of the extended field of view from both cameras. Furthermore, such improvement is reached using cameras with reduced sensor size which are more affordable. Hence, a dual-camera setup improves not only the pose estimation but also enables to use smaller sensors and reduce the overall system cost. Our analysis shows that even a slight modification in camera directions improves the positional accuracy further and yield attitude angle as accurate as ±6’ (compared to ±20’). The proposed pose estimation method relieves computational demands of traditional bundle adjustment processes and is easily integrated with other inertial sensors.
free text keywords: Process (computing), Monocular, Stereopsis, Inertial measurement unit, Computer science, Artificial intelligence, business.industry, business, Bundle adjustment, Computer vision, Pose, Simultaneous localization and mapping, GNSS applications, lcsh:Technology, lcsh:T, lcsh:Engineering (General). Civil engineering (General), lcsh:TA1-2040, lcsh:Applied optics. Photonics, lcsh:TA1501-1820
30 references, page 1 of 2

Artisense Corporation, 2019. Visual-Inertial Navigation System (VINS) DevKit. localization. Last Accessed: October 2019.

Barry, A. J., Oleynikova, H., Honegger, D., Pollefeys, M., Tedrake, R., 2015. Fast onboard stereo vision for uavs.

Civera, J., Davison, A. J., Montiel, J. M., 2008. Inverse depth parametrization for monocular SLAM. IEEE Transactions on Robotics, 24(5), 932-945.

Davison, A. J., Reid, I. D., Molton, N. D., Stasse, O., 2007. MonoSLAM: Real-time single camera SLAM. IEEE Transactions on Pattern Analysis & Machine Intelligence, 29(6), 1052-1067.

Engel, J., Scho¨ps, T., Cremers, D., 2014. LSD-SLAM: Large-scale direct monocular SLAM. European Conference on Computer Vision (ECCV), Springer, 834-849.

Engel, J., Stu¨ckler, J., Cremers, D., 2015. Large-scale direct SLAM with stereo cameras. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 1935-1942.

Frahm, J.-M., Fite-Georgel, P., Gallup, D., Johnson, T., Raguram, R., Wu, C., Jen, Y.-H., Dunn, E., Clipp, B., Lazebnik, S. et al., 2010. Building Rome on a cloudless day. European Conference on Computer Vision (ECCV), Springer, 368-381.

Gerke, M., Nex, F., Jende, P., 2016. Co-Registration of Terrestrial and UAV-Based Images - Experimental Results.

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-3/W4, 11-18.

Herath, H., Kodagoda, S., Dissanayake, G., 2007. Stereo vision based SLAM: Issues and solutions. Vision Systems: Applications, ITECH.

Howard, A., 2008. Real-time stereo visual odometry for autonomous ground vehicles. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 3946-3952.

Leutenegger, S., Chli, M., Siegwart, R., 2011. BRISK: Binary robust invariant scalable keypoints. 2011 IEEE international conference on computer vision (ICCV), IEEE, 2548-2555. [OpenAIRE]

Lourakis, M. I., Argyros, A. A., 2009. SBA: A software package for generic sparse bundle adjustment. ACM Transactions on Mathematical Software (TOMS), 36(1), 2.

Lowe, D. G., 2004. Distinctive image features from scaleinvariant keypoints. International journal of computer vision, 60(2), 91-110.

Muja, M., Lowe, D. G., 2009. Fast approximate nearest neighbors with automatic algorithm configuration. International Conference on Computer Vision Theory and Applications (VISAPP '09), 2, 331-340.

30 references, page 1 of 2
Any information missing or wrong?Report an Issue