Event Information:

• Wed

  28

  Jan

  2026

  SC Seminar: Abhay Balasaheb Jadhav

  15:45online

  Abhay Balasaheb Jadhav, RPTU University Kaiserslautern-Landau

  Title: Vehicle Localization in Radar Maps with Coarse Initial Estimates
  

  Abstract:

  Accurate localization in real-world environments is essential for autonomous vehicles to navigate safely, avoid collisions, and operate reliably. This holds even in harsh weather or areas with poor signal reception. Localization is typically achieved using global navigation satellite system (GNSS) receivers, which provide position estimates based on signals from satellites.
  However, GNSS receivers integrated into consumer vehicles often exhibit errors up to several meters. These errors affect localization accuracy. Coarse-to-fine scan alignment enables the computation of an accurate transformation from a noisy pose estimate, such as one derived from GNSS or place recognition systems. Although vision and LiDAR-based systems can perform effectively under ideal circumstances, they often face difficulties in adverse environmental conditions like fog, snow, or heavy rain. In contrast, automotive radars are robust under such challenging circumstances. Automotive radar sensors not only capture 3D position data but also provide additional information such as Doppler velocity, which is radial velocity along the line of sight of the sensor, and radar cross-section measurements, which depend on the material, surface, and shape of the object. These measurements can be utilized to aid localization accuracy in harsh weather conditions. However, radar point clouds present high sparsity and noise compared to LiDAR scans, leading to challenges in vehicle localization. This thesis aims to develop a coarse-to-fine scan registration method for aligning the current radar scan with the map scan from a previously recorded map database. The proposed method consists of two stages. In the first stage, a candidate map scan is selected based on a coarse initial estimate obtained from GNSS data and radar-specific properties. In the second stage, descriptors are extracted for each radar scan point from the candidate map scan and the current radar scan. These descriptors are robust to the sparsity and noise in the radar scans. Matching of these descriptors is done to estimate the relative transformation between the candidate map scan and the current radar scan. This estimated transformation can be used to localize the vehicle within the environment. Our coarse-to-fine scan alignment approach is evaluated on public automotive radar datasets, and our method shows state-of-the-art scan alignment accuracy on these datasets.

  How to join online

  You can join online via Zoom, using the following link:
  https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1