Liu Chang, Zhao Jin, Sun Nianyi, Yang Qingrong, Wang Leilei
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China.
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China.
Sensors (Basel). 2021 Mar 12;21(6):2025. doi: 10.3390/s21062025.
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 ( 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 divergence.
同步定位与地图构建（SLAM）在移动机器人领域有着广泛的应用。轻便且廉价的视觉传感器已被广泛用于全球定位系统（GPS）信号缺失或微弱的环境中的定位。移动机器人不仅要估计自身位姿，还要根据环境校正其位置，因此需要一个合适的数学模型来获取机器人在其环境中的状态。通常，基于滤波器的SLAM/视觉里程计（VO）在地图构建线程中将模型视为高斯分布，该线程处理均值和协方差之间的复杂关系。SLAM或VO中的协方差表示地图点的不确定性。因此，诸如概率论和信息论等方法在估计不确定性方面发挥着重要作用。在本文中，我们将信息论与经典视觉里程计（SVO）相结合，采用 Jensen-Shannon 散度（JS 散度）而非 Kullback-Leibler 散度（KL 散度）来估计深度的不确定性。一种更适合 SVO 的方法是探索提高移动设备在未知环境中的准确性和鲁棒性。同时，本文旨在有效利用小型便携性进行定位，并为先验应用场景提供先验知识。因此，结合 SVO 实现了 JS 散度，且已实现。它不仅具有准确区分异常值的特性，还能快速收敛内点。同时，结果表明，在相同的计算模拟下，与 KL 散度相结合相比，SVO 与 JS 散度相结合能够更准确地在环境中定位其状态。
