Search

This article targets fast indoor positioning and 3D target detection for unmanned aerial vehicle (UAV) real-time task implementation. With the combined direct method and feature method, a method is proposed for fast and accurate position estimation of the UAV. The camera pose is estimated by the visual odometer via the photometric error between the frames. Then the ORB features can be extended from the keyframes for the map consistency improvement by Bundle Adjustment with local and global optimization. A depth filter is also applied to assist the convergence of the map points with depth information updates from multiple frames. Moreover, the convolutional neural network is used to detect the specific target in an unknown space, while YOLOv3 is applied to obtain the semantic information of the target in the images. Thus, the spatial map points of the feature in the keyframes can be associated with the target detection box, while the statistical outlier filter can be simultaneously applied to eliminate the noise points. Experiments with public dataset, and field experiments on the established UAV platform in indoor environments have been carried out for visual based fast localization and object detection in real-time for the efficacy verification of the proposed method.

During task execution, the autonomous robots would likely pass through many narrow corridors along with mobile obstacles in dynamically complex environments. In this case, the off-line path planning algorithm is rather difficult to be directly implemented to acquire the available path in real-time. Hence, this article proposes a probabilistic roadmap algorithm based on the obstacle potential field sampling strategy to tackle the online path planning, called Obstacle Potential field-Probabilistic Roadmap Method (OP-PRM). The obstacle potential field is introduced to determine the obstacle area so as to construct the potential linked roadmap. Then the specific range around the obstacle boundary is justified as the target sampling area. Based on this obstacle localization, the effectiveness of the sampling points falling into the narrow corridors can be increased greatly for feasible roadmap construction. Furthermore, an incremental heuristic D* Lite algorithm is applied to search the shortest paths between the starting point and the target point on the roadmap. Simulation experiments demonstrate that the OP-PRM path planning algorithm can enable robots to search the optimal path fast from the starting point to the destination and effectively cross narrow corridors in complex dynamic environments.