Automatic Map Update Using Dashcam Videos
Aziza Zhanabatyrova, Clayton Souza Leite, Yu Xiao

TL;DR
This paper presents a vision-based mapping pipeline using dashcam videos that automatically detects changes in 3D maps, leveraging a novel deep learning localization method and robust clustering to improve accuracy in diverse environments.
Contribution
It introduces a new deep learning-based pixel-wise 3D localization algorithm trained on SfM data and a robust change detection pipeline for updating 3D maps from dashcam videos.
Findings
Achieved 85% and 100% change detection accuracy in two different environments.
Developed a novel deep learning method for high-accuracy 3D object localization from monocular images.
Demonstrated robustness of the system across varying conditions and camera types.
Abstract
Autonomous driving requires 3D maps that provide accurate and up-to-date information about semantic landmarks. Due to the wider availability and lower cost of cameras compared with laser scanners, vision-based mapping solutions, especially the ones using crowdsourced visual data, have attracted much attention from academia and industry. However, previous works have mainly focused on creating 3D point clouds, leaving automatic change detection as open issues. We propose in this paper a pipeline for initiating and updating 3D maps with dashcam videos, with a focus on automatic change detection based on comparison of metadata (e.g., the types and locations of traffic signs). To improve the performance of metadata generation, which depends on the accuracy of 3D object detection and localization, we introduce a novel deep learning-based pixel-wise 3D localization algorithm. The algorithm,…
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Taxonomy
TopicsRemote Sensing and LiDAR Applications · Robotics and Sensor-Based Localization · 3D Surveying and Cultural Heritage
