Spatial Imagination With Semantic Cognition for Mobile Robots

Autor:	Zhengcheng Shen, Linh Kastner, Jens Lambrecht
Rok vydání:	2021
Předmět:	FOS: Computer and information sciences Computer Science - Robotics Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Robotics (cs.RO)
Zdroj:	2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
DOI:	10.1109/iros51168.2021.9636550
Popis:	The imagination of the surrounding environment based on experience and semantic cognition has great potential to extend the limited observations and provide more information for mapping, collision avoidance, and path planning. This paper provides a training-based algorithm for mobile robots to perform spatial imagination based on semantic cognition and evaluates the proposed method for the mapping task. We utilize a photo-realistic simulation environment, Habitat, for training and evaluation. The trained model is composed of Resent-18 as encoder and Unet as the backbone. We demonstrate that the algorithm can perform imagination for unseen parts of the object universally, by recalling the images and experience and compare our approach with traditional semantic mapping methods. It is found that our approach will improve the efficiency and accuracy of semantic mapping. Comment: 7 pages, 14 figures
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c362ece9f1d716fdbb4b289176f5ccd https://doi.org/10.1109/iros51168.2021.9636550 Zobrazit plný text záznamu