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.
Highlights
In order to build intelligent applications for mobile robots in a large scene, autonomous and efficient navigation plays an increasingly important role
In order to deal with some dynamic objects that may exist in large scenes, the current algorithm usually eliminates dynamic objects by adding object detection or image segmentation algorithm based on Complexity deep learning in the system, such as Dynamic SLAM [6], Cluster VO [7], and Cluster SLAM [8] algorithm [9]
According to the analysis mentioned above, based on the existing Cartographer algorithm, this paper proposes a new method for 3D mapping in large scene contexts, which integrates a laser range finder and RGBD depth camera
Summary
In order to build intelligent applications for mobile robots in a large scene, autonomous and efficient navigation plays an increasingly important role. According to the analysis mentioned above, based on the existing Cartographer algorithm, this paper proposes a new method for 3D mapping in large scene contexts, which integrates a laser range finder and RGBD depth camera. (i) e approach proposed in this paper about reconstruction in large dynamic scene implements the fusion of RGBD camera and laser range finder based on the Cartographer algorithm. References [15, 16] proposed methods to calculate the pose change of the camera between two frames by line and surface features instead of point features These algorithms can solve the interference of moving objects in the scene, they will increase extra burden on computing and bring about disadvantageous effects on the accuracy and the real-time performance. With the assistance of global SLAM and local SLAM, the Cartographer algorithm has much better robustness, realtime performance, and accuracy in the real scene than the compared methods
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