contributor author | Jack C. P. Cheng | |
contributor author | Changhao Song | |
contributor author | Xiao Zhang | |
contributor author | Zhengyi Chen | |
date accessioned | 2023-11-27T23:10:56Z | |
date available | 2023-11-27T23:10:56Z | |
date issued | 5/19/2023 12:00:00 AM | |
date issued | 2023-05-19 | |
identifier other | JCCEE5.CPENG-5301.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4293362 | |
description abstract | Indoor localization is a prerequisite for autonomous robot applications in the construction industry. However, traditional localization techniques rely on low-level features and do not exploit construction-related semantics. They also are sensitive to environmental factors such as illumination and reflection rate, and therefore suffer from unexpected drifts and failures. This study proposes a pose graph relocalization framework that utilizes object-level landmarks to enhance a traditional visual localization system. The proposed framework builds an object landmark dictionary from Building Information Model (BIM) as prior knowledge. Then a multimodal deep neural network (DNN) is proposed to realize 3D object detection in real time, followed by instance-level object association with false-positive rejection, and relative pose estimation with outlier removal. Finally, a keyframe-based graph optimization is performed to rectify the drifts of traditional visual localization. The proposed framework was validated using a mobile platform with red-green-blue-depth (RGB-D) and inertial sensors, and the test scene was an indoor office environment with furnishing elements. The object detection model achieved 62.9% mean average precision (mAP). The relocalization technique reduced translational drifts by 64.67% and rotational drifts by 41.59% compared with traditional visual–inertial odometry. | |
publisher | ASCE | |
title | Pose Graph Relocalization with Deep Object Detection and BIM-Supported Object Landmark Dictionary | |
type | Journal Article | |
journal volume | 37 | |
journal issue | 5 | |
journal title | Journal of Computing in Civil Engineering | |
identifier doi | 10.1061/JCCEE5.CPENG-5301 | |
journal fristpage | 04023020-1 | |
journal lastpage | 04023020-18 | |
page | 18 | |
tree | Journal of Computing in Civil Engineering:;2023:;Volume ( 037 ):;issue: 005 | |
contenttype | Fulltext | |