Rapid Object Detection and Localization Through R-Tree Based Multi-Sensor Fusion for Inspection Robots

The detection and localization of objects are critical for inspection robots. Camera images facilitate rapid object detection but are limited in providing accurate object localization. Conversely, LiDAR point clouds can provide precise object localization but lack semantic information. To solve this problem, this work proposes a multi-sensor data fusion-based method for object detection and localization. The rich semantic information of camera images and the precise location of LiDAR point clouds are combined. An R-tree spatial indexing algorithm is used to match and process data swiftly. Accurate object localization can be achieved by using the provided method based on the fused data. Moreover, the method leverages point cloud data to refine confidence, significantly reducing the miss rate of detection in glaring conditions. Simulations and experiments are conducted to compare the proposed method with other localization algorithms. The results show that it significantly outperforms them in terms of accuracy.