Semi-semantic Line-Cluster Assisted Monocular SLAM for Indoor Environments
Conference paper
First Online:
Abstract
This paper presents a novel method to reduce the scale drift for indoor monocular simultaneous localization and mapping (SLAM). We leverage the prior knowledge that in the indoor environment, the line segments form tight clusters, e.g. many door frames in a straight corridor are of the same shape, size and orientation, so the same edges of these door frames form a tight line segment cluster. We implement our method in the popular ORB-SLAM2, which also serves as our baseline. In the front end we detect the line segments in each frame and incrementally cluster them in the 3D space. In the back end, we optimize the map imposing the constraint that the line segments of the same cluster should be the same. Experimental results show that our proposed method successfully reduces the scale drift for indoor monocular SLAM.
Keywords
SLAM Monocular Indoor Line-segment ClusteringReferences
- 1.Beevers, K.R., Huang, W.H.: Inferring and enforcing relative constraints in SLAM. In: Akella, S., Amato, N.M., Huang, W.H., Mishra, B. (eds.) Algorithmic Foundation of Robotics VII. STAR, vol. 47, pp. 139–154. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68405-3_9CrossRefGoogle Scholar
- 2.Botterill, T., Mills, S., Green, R.: Correcting scale drift by object recognition in single-camera SLAM. IEEE Trans. Cybern. 43(6), 1767–1780 (2013)CrossRefGoogle Scholar
- 3.Case, C., Suresh, B., Coates, A., Ng, A.Y.: Autonomous sign reading for semantic mapping. In: ICRA, pp. 3297–3303. IEEE (2011)Google Scholar
- 4.Civera, J., Gálvez-López, D., Riazuelo, L., Tardós, J.D., Montiel, J.: Towards semantic SLAM using a monocular camera. In: IROS, pp. 1277–1284. IEEE (2011)Google Scholar
- 5.De Boor, C., De Boor, C., Mathématicien, E.U., De Boor, C., De Boor, C.: A Practical Guide to Splines, vol. 27. Springer, New York (1978)CrossRefGoogle Scholar
- 6.Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 834–849. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_54CrossRefGoogle Scholar
- 7.Fioraio, N., Di Stefano, L.: Joint detection, tracking and mapping by semantic bundle adjustment. In: CVPR, pp. 1538–1545. IEEE (2013)Google Scholar
- 8.Gálvez-López, D., Salas, M., Tardós, J.D., Montiel, J.: Real-time monocular object SLAM. Robot. Auton. Syst. 75, 435–449 (2016)CrossRefGoogle Scholar
- 9.Kostavelis, I., Charalampous, K., Gasteratos, A., Tsotsos, J.K.: Robot navigation via spatial and temporal coherent semantic maps. Eng. Appl. Artif. Intell. 48, 173–187 (2016)CrossRefGoogle Scholar
- 10.Kostavelis, I., Gasteratos, A.: Semantic mapping for mobile robotics tasks: a survey. Robot. Auton. Syst. 66, 86–103 (2015)CrossRefGoogle Scholar
- 11.Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: G2O: a general framework for graph optimization. In: ICRA, pp. 3607–3613. IEEE (2011)Google Scholar
- 12.Lemaire, T., Lacroix, S.: Monocular-vision based SLAM using line segments. In: ICRA, pp. 2791–2796. IEEE (2007)Google Scholar
- 13.Lu, Y., Song, D.: Robust RGB-D odometry using point and line features. In: ICCV, pp. 3934–3942 (2015)Google Scholar
- 14.Lu, Y., Song, D.: Visual navigation using heterogeneous landmarks and unsupervised geometric constraints. IEEE Trans. Robot. 31(3), 736–749 (2015)CrossRefGoogle Scholar
- 15.Lu, Y., Song, D., Yi, J.: High level landmark-based visual navigation using unsupervised geometric constraints in local bundle adjustment. In: ICRA, pp. 1540–1545. IEEE (2014)Google Scholar
- 16.Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)CrossRefGoogle Scholar
- 17.Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017)CrossRefGoogle Scholar
- 18.Nützi, G., Weiss, S., Scaramuzza, D., Siegwart, R.: Fusion of IMU and vision for absolute scale estimation in monocular SLAM. J. Intell. Robot. Syst. 61(1–4), 287–299 (2011)CrossRefGoogle Scholar
- 19.Parsley, M.P., Julier, S.J.: Towards the exploitation of prior information in SLAM. In: IROS, pp. 2991–2996. IEEE (2010)Google Scholar
- 20.Pumarola, A., Vakhitov, A., Agudo, A., Sanfeliu, A., Moreno-Noguer, F.: PL-SLAM: real-time monocular visual SLAM with points and lines. In: ICRA, pp. 4503–4508. IEEE (2017)Google Scholar
- 21.Strasdat, H., Montiel, J., Davison, A.J.: Scale drift-aware large scale monocular SLAM. Robot.: Sci. Syst. VI 2 (2010)Google Scholar
- 22.Stückler, J., Biresev, N., Behnke, S.: Semantic mapping using object-class segmentation of RGB-D images. In: IROS, pp. 3005–3010. IEEE (2012)Google Scholar
- 23.Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: IROS, pp. 573–580. IEEE (2012)Google Scholar
- 24.Tomono, M., Yuta, S.: Mobile robot navigation in indoor environments using object and character recognition. In: ICRA, vol. 1, pp. 313–320. IEEE (2000)Google Scholar
- 25.Von Gioi, R.G., Jakubowicz, J., Morel, J.M., Randall, G.: LSD: a fast line segment detector with a false detection control. TPAMI 32(4), 722–732 (2010)CrossRefGoogle Scholar
- 26.Zhang, G., Lee, J.H., Lim, J., Suh, I.H.: Building a 3-D line-based map using stereo SLAM. IEEE Trans. Robot. 31(6), 1364–1377 (2015)CrossRefGoogle Scholar
- 27.Zhang, G., Suh, I.H.: A vertical and floor line-based monocular SLAM system for corridor environments. IJCAS 10(3), 547–557 (2012)Google Scholar
- 28.Zhang, J., Song, D.: On the error analysis of vertical line pair-based monocular visual odometry in urban area. In: IROS, pp. 3486–3491. IEEE (2009)Google Scholar
- 29.Zhang, J., Song, D.: Error aware monocular visual odometry using vertical line pairs for small robots in urban areas. In: AAAI (2010)Google Scholar
Copyright information
© Springer Nature Switzerland AG 2019