Abstract
In the study of SLAM problem using an RGB-D camera, depth information and visual information as two types of primary measurement data are rarely tightly coupled during refinement of camera pose estimation. In this paper, a new method of RGB-D camera SLAM is proposed based on extended bundle adjustment with integrated 2D and 3D information on the basis of a new projection model. First, the geometric relationship between the image plane coordinates and the depth values is constructed through RGB-D camera calibration. Then, 2D and 3D feature points are automatically extracted and matched between consecutive frames to build a continuous image network. Finally, extended bundle adjustment based on the new projection model, which takes both image and depth measurements into consideration, is applied to the image network for high-precision pose estimation. Field experiments show that the proposed method has a notably better performance than the traditional method, and the experimental results demonstrate the effectiveness of the proposed method in improving localization accuracy.
Highlights
Simultaneous localization and mapping (SLAM) is the process of incrementally estimating the pose of a moving platform and generating the surrounding map from the apparent motion induced on the images of its onboard cameras, and is considered to be a key prerequisite of truly autonomous robots [1,2,3,4]
Unlike the traditional bundle adjustment (BA) method [19,20,21] used in RGB-D camera SLAM, which refines the camera pose estimation using the projection model and error model that only constructed with visual information, depth information is used to generate 3D scene, and introduced into the BA model as one type of the primary measurement data in our method
As the relative geometric parameters are obtained through RGB-D camera calibration, every pixel in the depth image can be rendered with certain RGB value of the corresponding pixel in the RGB image, and colored 3D point clouds are generated from the registered RGB-D image
Summary
Simultaneous localization and mapping (SLAM) is the process of incrementally estimating the pose of a moving platform and generating the surrounding map from the apparent motion induced on the images of its onboard cameras, and is considered to be a key prerequisite of truly autonomous robots [1,2,3,4]. Dryanovski et al [24] realized indoor SLAM using Kalman Filter and loop closure detection to optimize the camera pose estimation obtained by ICP algorithm. Unlike the traditional BA method [19,20,21] used in RGB-D camera SLAM, which refines the camera pose estimation using the projection model and error model that only constructed with visual information, depth information is used to generate 3D scene, and introduced into the BA model as one type of the primary measurement data in our method. Results of field experiments are given to verify the accuracy and effectiveness of this new method
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