22 research outputs found
Sparse Semantic Map-Based Monocular Localization in Traffic Scenes Using Learned 2D-3D Point-Line Correspondences
Vision-based localization in a prior map is of crucial importance for
autonomous vehicles. Given a query image, the goal is to estimate the camera
pose corresponding to the prior map, and the key is the registration problem of
camera images within the map. While autonomous vehicles drive on the road under
occlusion (e.g., car, bus, truck) and changing environment appearance (e.g.,
illumination changes, seasonal variation), existing approaches rely heavily on
dense point descriptors at the feature level to solve the registration problem,
entangling features with appearance and occlusion. As a result, they often fail
to estimate the correct poses. To address these issues, we propose a sparse
semantic map-based monocular localization method, which solves 2D-3D
registration via a well-designed deep neural network. Given a sparse semantic
map that consists of simplified elements (e.g., pole lines, traffic sign
midpoints) with multiple semantic labels, the camera pose is then estimated by
learning the corresponding features between the 2D semantic elements from the
image and the 3D elements from the sparse semantic map. The proposed sparse
semantic map-based localization approach is robust against occlusion and
long-term appearance changes in the environments. Extensive experimental
results show that the proposed method outperforms the state-of-the-art
approaches