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Self-driving vehicle systems have become mainstream as major international research and development companies compete for road detection and mapping projects such as localization. The latter plays an important role in self-driving systems because an accurate determination of the vehicle's position improves trajectory and offset results. And with a good localization function, a high-precision map can be established. This paper proposes using pole maps to enhance vehicle localization while correcting previous vehicle localization trajectory offsets. We have established a pole feature map with pole-shaped characteristic point clouds along the road that are marked with spatial coordinate information. We use three-dimensional light detection and ranging (3D Lidar) as a sensor for environmental scanning, dynamic segment thresholds, and point cloud projections to the image to extract poles, which avoids identification errors typically caused by the scattering effect of 3D Lidar. The resulting pole map uses the occupancy grid map to filter out noise. In terms of localization, we continue to detect pole features while the vehicle is moving, and compare these with the map. When the results match, we update the current localized information. The localization point corrects the information of the trajectory of the undetected pole, which improves vehicle localization accuracy.

Nowadays, unmanned vehicles for navigation applications, such as factory unmanned vehicles and delivery dining systems, are becoming more and more extensive and gradually playing an indispensable role in our lives. Basically, navigation can be divided into three fields, the mapping, the localization and the path planning, in which the path planning is to use a pre-built map to plan a feasible path given the starting and the ending points. Hence, the path planning is the core part of the navigation operation, which is very important at the robot application level, such as the automatic driving and the driverless driving. For aircraft and space exploration, the path planning algorithms can be roughly divided into two types, the graph-based searching and the sampling-based searching. Among the two, the path planning based on random sampling provides with fast operation speed, high success rate on high-dimensional and complex problems, and disuse of extra considerations. The constraint of non-holonomic constraints, and the fast search for random trees are an algorithm based on random sampling. This paper mainly focuses on improving the path divergence in fixed iteration and on leading the direction from root to goal in fixed iteration condition. From the comparison of the experimental results, our approach is approximately 1.5 times better than the RRT on average path length.

Automated driving has been developed rapidly in the past five years. Many automakers have popularized SAE Level 2 on self-driving cars, some even on regular vehicles. To implement Level 3 or Level 4, it’s necessary to rely on a good localization system when GPS signal is not available. For this need, this paper proposes two effective methods to increase the accuracy of the real-time SLAM: The first method: When the original point cloud is input, the point cloud is divided into the short-range group, the medium-range group and the long-range group. An adaptive parameter adjustment method is then used to obtain the optimal parameters for each of these point cloud groups. However, Lidar’s physical characteristics can cause the point cloud to be insufficient, making an important part of points misjudged as outliers for the medium-range and long-range cases. Thanks to the help of the adaptive parameters, these point clouds, which were originally misjudged as outliers can be preserved in this paper. The second method: In point clouds, the same object in different ranges is represented with different point clouds. Hence, features, such as the roughness and density, can dramatically change with the variation of distance even when it is the same object. To solve this problem, we have designed three different range point cloud feature extraction methods to get more accurate point cloud features, such as the planes or edges. By combining these two steps, the LeGO-LOAM accuracy can be effectively increased by more than 30% while achieving the performance of the real-time SLAM, which is more accurate and faster than the NDT-Mapping used in Autoware.

This article analyzes the methods for project supervision in the geological prospecting industry at present, and proposes an embedded supervisal system of geological prospecting designed with the mobile terminals as an assistant supervisal measure, to increase the efficiency of project supervision. Supervisal indicators are categorized and studied, and an indicator system for the geological prospecting is introduced. The method for implementing AMF protocol based system is presented to solve huge data filling in the field.