ISVD-Based Advanced Simultaneous Localization and Mapping (SLAM) Algorithm for Mobile Robots

Abstract

In the case of simultaneous localization and mapping, route planning and navigation are based on data captured by multiple sensors, including built-in cameras. Nowadays, mobile devices frequently have more than one camera with overlapping fields of view, leading to solutions where depth information can also be gathered along with ordinary RGB color data. Using these RGB-D sensors, two- and three-dimensional point clouds can be recorded from the mobile devices, which provide additional information for localization and mapping. The method of matching point clouds during the movement of the device is essential: reducing noise while having an acceptable processing time is crucial for a real-life application. In this paper, we present a novel ISVD-based method for displacement estimation, using key points detected by SURF and ORB feature detectors. The ISVD algorithm is a fitting procedure based on SVD resolution, which removes outliers from the point clouds to be fitted in several steps. The developed method removes these outlying points in several steps, in each iteration examining the relative error of the point pairs and then progressively reducing the maximum error for the next matching step. An advantage over relevant methods is that this method always gives the same result, as no random steps are included.

Brain-Inspired Navigation Model Based on the Distribution of Polarized Sky-Light

“…The output frequency of satellite navigation is low, usually 10 Hz, but for a system with a control frequency of 100-200 Hz, the ideal output frequency is 20-40 Hz; otherwise, the control effect of the system will be affected; and if the satellite antenna is blocked, the signal will be interrupted [3]. In addition, the simultaneous localization and mapping (SLAM) method based on a visual odometer (VO) [4,5] or visual-inertial odometer (VIO) [6,7] is also an important way to solve the autonomous navigation of unmanned platforms, but the common SLAM methods have problems such as a large amount of computation and low robustness in complex and large scenes [8]. Therefore, exploring an autonomous and reliable navigation method suitable for unmanned platforms in large scenes has become an urgent problem that needs to be solved.…”