Work

A simple information fusion method provides the obstacle with saliency labeling as a landmark in robotic mapping

Year: 2022

Type: article

Abstract: Mapping is the key technology of the robot slam algorithm, mainly used to obtain environmental geometric information. How to effectively improve the ability to obtain semantic information on the map becomes essential. Salient object segmentation with depth can be used as a kind of salient semantic information that helps the robot better understand the environment. This paper put forward a consistent salient determination method based on visual and lidar images for complementary advantages. Our method is based on the simple information fusion of the two sensors' salient object key features, including approximate centroids and contour widths. Theoretical analysis and experimental results demonstrate that the proposed method has the advantage of strong illumination robustness and can work under weak light conditions, which can solve the task in the visual image when the front salient object blocks the back salient object. It can be used to detect salient objects with approximate size in indoor and outdoor sparse scenes. The detected salient objects can be used as semantic labeling labeled in the environment and as the relative position reference for helping the robot improve the accuracy of mapping, positioning, and planning the motion more intelligently.

Source: Alexandria Engineering Journal

DOI: 10.1016/j.aej.2022.06.002

Authors: Lihe Hu, Yi Zhang, Yang Wang, Qin Jiang, Gengyu Ge, Wei Wang

Institution: Chongqing University of Posts and Telecommunications

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $990

Funders: National Natural Science Foundation of China, Faculty of Engineering, Alexandria University, Chongqing Municipal Education Commission, Alexandria University