  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, trained
directly with SfM point cloud data, can locate objects detected from 2D images
in a 3D space with high accuracy by estimating not only depth from monocular
images but also lateral and height distances. In addition, we also propose a
point clustering and thresholding algorithm to improve the robustness of the
system to errors. We have performed experiments on two distinct areas - a
campus and a residential area - with different types of cameras, lighting, and
weather conditions. The changes were detected with 85% and 100% accuracy in the
campus and residential areas, respectively. The errors in the campus area were
mainly due to traffic signs seen from a far distance to the vehicle and
intended for pedestrians and cyclists only. We also conducted cause analysis of
the detection and localization errors to measure the impact from the
performance of the background technology in use.
