Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM

Localization for estimating the position and orientation of a robot in an asymmetrical environment has been solved by using various 2D laser rangefinder simultaneous localization and mapping (SLAM) approaches. Laser-based SLAM generates an occupancy grid map, then the most popular Monte Carlo Localization (MCL) method spreads particles on the map and calculates the position of the robot by a probabilistic algorithm. However, this can be difficult, especially in symmetrical environments, because landmarks or features may not be sufficient to determine the robot’s orientation. Sometimes the position is not unique if a robot does not stay at the geometric center. This paper presents a novel approach to solving the robot localization problem in a symmetrical environment using the visual features-assisted method. Laser range measurements are used to estimate the robot position, while visual features determine its orientation. Firstly, we convert laser range scans raw data into coordinate data and calculate the geometric center. Secondly, we calculate the new distance from the geometric center point to all end points and find the longest distances. Then, we compare those distances, fit lines, extract corner points, and calculate the distance between adjacent corner points to determine whether the environment is symmetrical. Finally, if the environment is symmetrical, visual features based on the ORB keypoint detector and descriptor will be added to the system to determine the orientation of the robot. The experimental results show that our approach can successfully determine the position of the robot in a symmetrical environment, while ordinary MCL and its extension localization method always fail.

A New Square Grid-Based Method for Identifying Solar Activities

The FITS is widely used as a common file format in astronomy. However, how to detect an effective celestial event from large-scale astronomical images with complex background is a thorny issue. This paper proposes a square grid structure for target detection (GSTD) of solar activities, inncluding the separation of the target area and the deletion of the background area. This kind of processing can greatly reduce the storage cost of the data set, while the solar activity area can be well preserved. The solar image is operated through an equally sized square grid to separate the solar activity target area and the background area to speed up the processing of images, improve processing accuracy, and effectively prevent image noise interference. The experiment results have shown that this method delivers satisfactory performance in accuracy and time-cost. Through the anti-jamming processing of image noise, the accurate positioning and effective recognition of solar activities are realized. This method has been proved to achieve good image segmentation recognition of solar phenomenon in the research of various types of solar activities. Moreover, it can provide a feasible way to reduce the storage occupancy of Content-Based Image Retrieval (CBIR) systems.