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GCP-SLAM: LSD-SLAM with Learning-Based Confidence Estimation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)

Abstract

Astonishing progress has been made in direct monocular SLAMs in the last few years. However, most direct methods, such as large-scale direct monocular SLAM (LSD-SLAM), usually have lower camera localization accuracy than feature-based methods. To tackle this issue, this paper suggests a novel LSD-SLAM model, i.e., GCP-SLAM, by incorporating with learning-based confidence estimation. A regression forest method is used to estimate confidence and select ground control points (GCPs). The estimated confidence and GCPs are then exploited for improving depth estimation and camera localization, respectively. Experiments show that GCP-SLAM is more reliable in tracking and relocalization than LSD-SLAM.

Keywords

Monocular SLAM Ground control points (GCPs) Localization Random forest 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.School of Mechatronics EngineeringHarbin Institute of TechnologyHarbinChina

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