Indirect Visual Simultaneous Localization and Mapping Based on Linear Models

• Published in: IEEE sensors journal Vol. 20; no. 5; pp. 2738 - 2747
• Main Authors: Chien, Chiang-Heng, Hsu, Chen-Chien James, Wang, Wei-Yen, Chiang, Hsin-Han
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cameras; Feature extraction; Frame design; indirect method; Iterative methods; Landmarks; Linear equations; Linearity; Localization; loop closure; map building; Mathematical model; Optimization; Outliers (statistics); Reliability; Simultaneous localization and mapping; Visual simultaneous localization and mapping; Visualization; Weighting functions
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2019.2952722

This paper proposes an indirect visual simultaneous localization and mapping (V-SLAM) for large-scale outdoor environments based on linear models. With the scale-invariant feature transform (SIFT) algorithm, a design combining key-frame insertion and map management is proposed to avoid redundant computation and unreliable landmarks. Then, an iterative linear equation is employed to update landmarks. With the supplement of reliable map data, absolute camera poses can be obtained using a simple linear equation based on a linearity index. In addition, a linear model helps the system to detect candidates of looped frames and discard invalid loops by means of an outlier weight function. If a loop is detected, an improved trajectory bending algorithm is introduced to optimize the estimations of poses and landmarks. To evaluate the effectiveness of the proposed approach, extensive experiments are conducted using various sequences from a well-known public dataset. Experimental results show that the proposed V-SLAM outperforms other existing methods in terms of various metrics.