Robotic sensing and object recognition from thermal-mapped point clouds

Many of the civil structures are more than half way through or nearing their intended service life; frequently assessing and maintaining structural integrity is a top maintenance priority. Robotic inspection technologies using ground and aerial robots with 3D scanning and imaging capabilities have the potential to improve safety and efficiency of infrastructure management. To provide more valuable information to inspectors and agency decision makers, automatic environment sensing and semantic information extraction are fundamental issues in this field. This paper introduces an innovative method for generating thermal-mapped point clouds of a robot’s work environment and performing automatic object recognition with the aid of thermal data fused to 3D point clouds. The laser scanned point cloud and thermal data were collected using a custom-designed mobile robot. The multimodal data was combined with a data fusion process based on texture mapping. The automatic object recognition was performed by two processes: segmentation with thermal data and classification with scanned geometric features. The proposed method was validated with the scan data collected in an entire building floor. Experimental results show that the thermal integrated object recognition approach achieved better performance than a geometry only-based approach, with an average recognition accuracy of 93%, precision of 83%, and recall rate of 86% for objects in the tested environment including humans, display monitors and light fixtures. © 2017, Springer Singapore.