IT-SVO: Improved Semi-Direct Monocular Visual Odometry Combined with JS Divergence in Restricted Mobile Devices

• Main Authors: Chang Liu, Jin Zhao, Nianyi Sun, Qingrong Yang, Leilei Wang
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Sensors
• Subjects: SLAM; localization; information theory; JS divergence; tracking
• Online Access: https://www.mdpi.com/1424-8220/21/6/2025

Simultaneous localization and mapping (SLAM) has a wide range for applications in mobile robotics. Lightweight and inexpensive vision sensors have been widely used for localization in GPS-denied or weak GPS environments. Mobile robots not only estimate their pose, but also correct their position according to the environment, so a proper mathematical model is required to obtain the state of robots in their circumstances. Usually, filter-based SLAM/VO regards the model as a Gaussian distribution in the mapping thread, which deals with the complicated relationship between mean and covariance. The covariance in SLAM or VO represents the uncertainty of map points. Therefore, the methods, such as probability theory and information theory play a significant role in estimating the uncertainty. In this paper, we combine information theory with classical visual odometry (SVO) and take Jensen-Shannon divergence (JS divergence) instead of Kullback-Leibler divergence (<i>KL </i>divergence) to estimate the uncertainty of depth. A more suitable methodology for SVO is that explores to improve the accuracy and robustness of mobile devices in unknown environments. Meanwhile, this paper aims to efficiently utilize small portability for location and provide a priori knowledge of the latter application scenario. Therefore, combined with SVO, JS divergence is implemented, which has been realized. It not only has the property of accurate distinction of outliers, but also converges the inliers quickly. Simultaneously, the results show, under the same computational simulation, that SVO combined with JS divergence can more accurately locate its state in the environment than the combination with <i>KL </i>divergence.