{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/egocentric-spatial-memory","title":"Egocentric Spatial Memory","arxiv_id":"1807.11929","date":"2018-07-31","proceeding":null,"authors":["Mengmi Zhang","Keng Teck Ma","Shih-Cheng Yen","Joo Hwee Lim","Qi Zhao","Jiashi Feng"],"abstract":"Egocentric spatial memory (ESM) defines a memory system with encoding,\nstoring, recognizing and recalling the spatial information about the\nenvironment from an egocentric perspective. We introduce an integrated deep\nneural network architecture for modeling ESM. It learns to estimate the\noccupancy state of the world and progressively construct top-down 2D global\nmaps from egocentric views in a spatially extended environment. During the\nexploration, our proposed ESM model updates belief of the global map based on\nlocal observations using a recurrent neural network. It also augments the local\nmapping with a novel external memory to encode and store latent representations\nof the visited places over long-term exploration in large environments which\nenables agents to perform place recognition and hence, loop closure. Our\nproposed ESM network contributes in the following aspects: (1) without feature\nengineering, our model predicts free space based on egocentric views\nefficiently in an end-to-end manner; (2) different from other deep\nlearning-based mapping system, ESMN deals with continuous actions and states\nwhich is vitally important for robotic control in real applications. In the\nexperiments, we demonstrate its accurate and robust global mapping capacities\nin 3D virtual mazes and realistic indoor environments by comparing with several\ncompetitive baselines.","url_abs":"http://arxiv.org/abs/1807.11929v1","url_pdf":"http://arxiv.org/pdf/1807.11929v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"egocentric-spatial-memory","repo_url":"https://github.com/Mengmi/Egocentric-Spatial-Memory","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.11929","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}