RGB-D SLAM Combining Visual Odometry and Extended Information Filter.
In this paper, we present a novel RGB-D SLAM system based on visual odometry and an extended information filter, which does not require any other sensors or odometry. In contrast to the graph optimization approaches, this is more suitable for online applications. A visual dead reckoning algorithm based on visual residuals is devised, which is used to estimate motion control input. In addition, we use a novel descriptor called binary robust appearance and normals descriptor (BRAND) to extract features from the RGB-D frame and use them as landmarks. Furthermore, considering both the 3D positions and the BRAND descriptors of the landmarks, our observation model avoids explicit data association between the observations and the map by marginalizing the observation likelihood over all possible associations. Experimental validation is provided, which compares the proposed RGB-D SLAM algorithm with just RGB-D visual odometry and a graph-based RGB-D SLAM algorithm using the publicly-available RGB-D dataset. The results of the experiments demonstrate that our system is quicker than the graph-based RGB-D SLAM algorithm.
School of Information Engineering, East China Jiaotong University, Nanchang 330013, China. jdtan122@utk.edu.|Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA. jdtan122@utk.edu.
Heng
H
Zhang
School of Information Engineering, East China Jiaotong University, Nanchang 330013, China. yliu133@utk.edu.|Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA. yliu133@utk.edu.
Yanli
Y
Liu
Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA. jdtan122@utk.edu.
Jindong
J
Tan
School of Computer Science, Colorado Technical University, Colorado Springs, CO 80907, USA. dnxiong@ieee.org.
Naixue
N
Xiong
SLAM; binary descriptor; extended information filter; visual odometry