Scale Estimation and Correction of the Monocular Simultaneous Localization and Mapping (SLAM) Based on Fusion of 1D Laser Range Finder and Vision Data.

Zhuang Zhang,Kun Yan,Rujin Zhao,Yuebo Ma,Enhai Liu

doi:10.3390/s18061948

Abstract

This article presents a new sensor fusion method for visual simultaneous localization and mapping (SLAM) through integration of a monocular camera and a 1D-laser range finder. Such as a fusion method provides the scale estimation and drift correction and it is not limited by volume, e.g., the stereo camera is constrained by the baseline and overcomes the limited depth range problem associated with SLAM for RGBD cameras. We first present the analytical feasibility for estimating the absolute scale through the fusion of 1D distance information and image information. Next, the analytical derivation of the laser-vision fusion is described in detail based on the local dense reconstruction of the image sequences. We also correct the scale drift of the monocular SLAM using the laser distance information which is independent of the drift error. Finally, application of this approach to both indoor and outdoor scenes is verified by the Technical University of Munich dataset of RGBD and self-collected data. We compare the effects of the scale estimation and drift correction of the proposed method with the SLAM for a monocular camera and a RGBD camera.

Highlights

Simultaneous localization and mapping (SLAM) may help robots create their own maps while locating themselves in unknown areas by using specific sensors
This study introduced a novel SLAM method for achieving the absolute scale estimation and the scaling drift correction through the fusion of 1D laser distance information and monocular vision
We first describe integration of the novel fusion. Such a SLAM method does not have to increase the baseline distance in order to measure a large scene like the binocular vision, and is no longer limited by sensors such as RGBD

Summary

Introduction

Simultaneous localization and mapping (SLAM) may help robots create their own maps while locating themselves in unknown areas by using specific sensors. The monocular SLAM has attracted a widespread attention thanks to its low cost, wide application, and rich information, and has recently made great strides. Similar to more popular cases, e.g., EKF-SLAM [1,2], ORB-SLAM [3,4] and LSD-SLAM [5], a complete monocular. The monocular SLAM suffers from a particular disadvantage, whereby the camera cannot estimate the absolute scale and the scale will drift.

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Methods

Conclusion