期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 6, 期 2, 页码 3341-3348出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3061080
关键词
Mapping; deep learning for visual perception; recognition
类别
资金
- University of Adelaide
- Australian Centre for Robotic Vision
- Monash University
Semantic aware reconstruction provides more advantages for future robotic and AR/VR applications compared to geometric-only reconstruction, as it not only indicates the locations of objects, but also identifies the objects themselves. MOLTR is capable of localizing, tracking, and reconstructing multiple rigid objects using monocular image sequences and camera poses.
Semantic aware reconstruction is more advantageous than geometric-only reconstruction for future robotic and AR/VR applications because it represents not only where things are, but also what things are. Object-centric mapping is a task to build an object-level reconstruction where objects are separate and meaningful entities that convey both geometry and semantic information. In this letter, we present MOLTR, a solution to object-centric mapping using only monocular image sequences and camera poses. It is able to localize, track and reconstruct multiple rigid objects in an online fashion when a RGB camera captures a video of the surrounding. Given a new RGB frame, MOLTR firstly applies a monocular 3D detector to localize objects of interest and extract their shape codes that represent the object shape in a learnt embedding space. Detections are then merged to existing objects in the map after data association. Motion state (i.e., kinematics and the motion status) of each object is tracked by a multiple model Bayesian filter and object shape is progressively refined by fusing multiple shape code. We evaluate localization, tracking and reconstruction on benchmarking datasets for indoor and outdoor scenes, and show superior performance over previous approaches.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据