Semantic surrounding projection for object height estimation using single-line lidar and image

Abstract

Indoor environment perception requires surroundings to facilitate the path planning and object management using three dimensional lidar or visual sensors,which addresses the need for accurate height estimation in tasks like estimating cabinet height for grabbing things and judging passageway passing height. The proposed method combines single-line lidar and image to extract features, such as line segmentation, objects detection and semantic segmentation map, using neural network. Height estimation is achieved by referencing single image with single-line lidar data. The multi-source data approach enhances the accuracy especially in the areas where data from a single sensor are unreliable. A set of analysis of errors and uncertainties introduced by the method are conducted to facilitate the indoor perception system through the integration of muti-source data and neural network-base feature extraction for height estimation, and results demonstrate the performance and efficiency of the method.