Real-time monocular object SLAM |
| |
Affiliation: | 1. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater OK, 74078, USA;2. College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China;3. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China;1. Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, 27695, USA;2. School of Computing, University of Utah, Salt Lake City, UT, 84112, USA |
| |
Abstract: | We present a real-time object-based SLAM system that leverages the largest object database to date. Our approach comprises two main components: (1) a monocular SLAM algorithm that exploits object rigidity constraints to improve the map and find its real scale, and (2) a novel object recognition algorithm based on bags of binary words, which provides live detections with a database of 500 3D objects. The two components work together and benefit each other: the SLAM algorithm accumulates information from the observations of the objects, anchors object features to especial map landmarks and sets constrains on the optimization. At the same time, objects partially or fully located within the map are used as a prior to guide the recognition algorithm, achieving higher recall. We evaluate our proposal on five real environments showing improvements on the accuracy of the map and efficiency with respect to other state-of-the-art techniques. |
| |
Keywords: | Object slam Object recognition |
本文献已被 ScienceDirect 等数据库收录! |
|