UAV Based Indoor Localization and Objection Detection

Abstract

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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.

Keywords

• visual SLAM
• self-positioning
• real-time localization
• convolutional neural network
• target detection
• UAV