The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 757–763, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-757-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 757–763, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-757-2020

  12 Aug 2020

12 Aug 2020

DIRECT SPARSE VISUAL ODOMETRY WITH STRUCTURAL REGULARITIES FOR LONG CORRIDOR ENVIRONMENTS

F. Cheng1, C. Liu1, H. Wu1, and M. Ai1,2 F. Cheng et al.
  • 1College of Surveying and Geo-informatics, Tongji University, 200092 Shanghai, China
  • 2Information Sciences and Technology, The Pennsylvania State University, State College, USA

Keywords: Visual Odometry, Simultaneous Localization and Mapping, Manhattan World, Structural Lines

Abstract. Simultaneous Localization and Mapping are the key requirements for many practical applications of robotics. However, traditional visual approaches rely on features extracted from textured surfaces, so they barely work well in indoor scenes (e.g. long corridors containing large proportions of smooth walls). In this work, we propose a novel visual odometry method to overcome these limitations, which integrates structural regularities of man-made environments in a direct sparse visual odometry system. By fully exploiting structural lines that align with the dominant direction in the Manhattan world, our approach becomes more accurate and robust to texture-less indoor environments, specially, long corridors. Given a series of image inputs, we first use the direct sparse method to obtain the coarse relative pose between camera frames, and then calculate vanishing points on each frame. Secondly, we use structural lines as rotation constraints, and perform a sliding window optimization to reduce both photometric and rotation errors, to further improve the trajectory accuracy. Through the benchmark test, it is proved that our method performs better than that of the existing visual odometry approach in long corridor environments.