Title | Knitter: Fast, Resilient Single-User Indoor Floor Plan Construction |
Authors | Gao, Ruipeng Zhou, Bing Ye, Fan Wang, Yizhou |
Affiliation | Beijing Jiaotong Univ, Sch Software Engn, Beijing, Peoples R China. SUNY Stony Brook, ECE Dept, Stony Brook, NY 11794 USA. Peking Univ, EECS Sch, Beijing, Peoples R China. |
Issue Date | 2017 |
Publisher | IEEE INFOCOM 2017 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS |
Citation | IEEE INFOCOM 2017 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS. 2017. |
Abstract | Lacking of floor plans is a fundamental obstacle to ubiquitous indoor location-based services. Recent work have made significant progress to accuracy, but they largely rely on slow crowdsensing that may take weeks or even months to collect enough data. In this paper, we propose Knitter that can generate accurate floor maps by a single random user's one hour data collection efforts. Knitter extracts high quality floor layout information from single images, calibrates user trajectories and filters outliers. It uses a multi-hypothesis map fusion framework that updates landmark positions/orientations and accessible areas incrementally according to evidences from each measurement. Our experiments on 3 different large buildings and 30+ users show that Knitter produces correct map topology, and 90-percentile landmark location and orientation errors of 3 similar to 5m and 4 similar to 6 degrees, comparable to the state-of-the-art at more than 20 speed up: data collection can finish in about one hour even by a novice user trained just a few minutes. |
URI | http://hdl.handle.net/20.500.11897/512022 |
ISSN | 0743-166X |
Indexed | CPCI-S(ISTP) |
Appears in Collections: | 信息科学技术学院 |