A 3D Reconstruction Method Using Multisensor Fusion in Large-Scale Indoor Scenes

Abstract

RGBD camera-based VSLAM (Visual Simultaneous Localization and Mapping) algorithm is usually applied to assist robots with real-time mapping. However, due to the limited measuring principle, accuracy, and distance of the equipped camera, this algorithm has typical disadvantages in the large and dynamic scenes with complex lightings, such as poor mapping accuracy, easy loss of robot position, and much cost on computing resources. Regarding these issues, this paper proposes a new method of 3D interior construction, which combines laser radar and an RGBD camera. Meanwhile, it is developed based on the Cartographer laser SLAM algorithm. The proposed method mainly takes two steps. The first step is to do the 3D reconstruction using the Cartographer algorithm and RGBD camera. It firstly applies the Cartographer algorithm to calculate the pose of the RGBD camera and to generate a submap. Then, a real-time 3D point cloud generated by using the RGBD camera is inserted into the submap, and the real-time interior construction is finished. The second step is to improve Cartographer loop-closure quality by the visual loop-closure for the sake of correcting the generated map. Compared with traditional methods in large-scale indoor scenes, the proposed algorithm in this paper shows higher precision, faster speed, and stronger robustness in such contexts, especially with complex light and dynamic objects, respectively.

UncLe-SLAM: Uncertainty Learning for Dense Neural SLAM

“…Yet, these methods study specific sensors and are not inherently equipped with 3D reasoning. Few works consider 3D reconstruction with multiple sensors [57,31,7,76,77,24], but these do not consider the online mapping setting. Conceptually, more closely related to our work is SenFu-Net [58], which is an online mapping method for multisensor depth fusion.…”

Multiscale Receptive Fields Graph Attention Network for Point Cloud Classification

“…Point cloud as a simple and efficient representation for 3D shapes and scenes has become more and more popular in the fields of both academia and industry. For example, autonomous vehicle [1][2][3][4], robotic mapping and navigation [5][6][7], 3D shape representation and modelling [8,9], and other relevant applications [10][11][12][13][14][15]. Lots of ways can be used to obtain 3D point cloud data, such as utilizing 3D scanners including physical touch or noncontact measurements with light, sound, LiDAR, etc.…”

Retracted: A 3D Reconstruction Method Using Multisensor Fusion in Large-Scale Indoor Scenes

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”