Precise localization is essential but also challenging for autonomous vehicles. In this article,
A novel visual localization method is proposed. Specifically, a semantic local map …

Monocular Re-Localization (MRL) is an essential component in autonomous applications,
estimating 6 degree-of-freedom ego poses wrt the scene map using monocular images …

Many map matching-based localization algorithms estimate the autonomous car's pose
using a registration between lanes measured by a camera and the High-Definition (HD) …

Vehicle localization in highway tunnels is challenging for autonomous vehicle navigation.
The drift error increases because GPS signals from satellites cannot be received inside a …

Autonomous Driving (AD) introduces dramatic changes to the way we travel. This emerging
technology has the potential to impact the transportation sector across a wide array of …

Accurate localization ability is fundamental in autonomous driving. Traditional visual
localization frameworks approach the semantic map-matching problem with geometric …

We present an end-to-end learning architecture for wheel speeds and inertial fusion for real-
time ego-motion estimation. In contrast to the previous work, our method utilizes the neural …

While a majority of transportation and mobility solutions rely on in-vehicle sensors and the
availability of the global positioning system (GPS) for absolute localization, alternate …

In self-driving, standalone GPS is generally considered to have insufficient positioning
accuracy to stay in lane. Instead, many turn to LIDAR localization, but this comes at the …

Accurate and reliable localization is necessary for vehicle autonomous driving. Existing
localization systems based on the GNSS cannot always provide lane-level accuracy. This …