Probabilistic semantic mapping for urban autonomous driving applications

David Paz, Hengyuan Zhang, Qinru Li, Hao Xiang, Henrik I Christensen

Abstract:

Recent advancements in statistical learning and
computational abilities have enabled autonomous vehicle technology to develop at a much faster rate. While many of
the architectures previously introduced are capable of operating under highly dynamic environments, many of these
are constrained to smaller-scale deployments, require constant
maintenance due to the associated scalability cost with highdefinition (HD) maps, and involve tedious manual labeling.
As an attempt to tackle this problem, we propose to fuse
image and pre-built point cloud map information to perform
automatic and accurate labeling of static landmarks such as
roads, sidewalks, crosswalks, and lanes. The method performs
semantic segmentation on 2D images, associates the semantic
labels with point cloud maps to accurately localize them in
the world, and leverages the confusion matrix formulation to
construct a probabilistic semantic map in bird’s eye view from
semantic point clouds. Experiments from data collected in an
urban environment show that this model is able to predict
most road features and can be extended for automatically
incorporating road features into HD maps with potential future
work directions.