25 citations · 39 across the 13 of their papers we have counts for
6 papers · 1 filter
CaLiV: LiDAR-to-Vehicle Calibration of Arbitrary Sensor Setups
Ilir Tahiraj, Markus Edinger, Dominik Kulmer +1
In autonomous systems, sensor calibration is essential for safe and efficient navigation in dynamic environments. Accurate calibration is a prerequisite for reliable perception and…
Cal or No Cal? -- Real-Time Miscalibration Detection of LiDAR and Camera Sensors
Ilir Tahiraj, Jeremialie Swadiryus, Felix Fent +1
The goal of extrinsic calibration is the alignment of sensor data to ensure an accurate representation of the surroundings and enable sensor fusion applications. From a safety pers…
FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps
Maximilian Leitenstern, Marko Alten, Christian Bolea-Schaser +3
Current software stacks for real-world applications of autonomous driving leverage map information to ensure reliable localization, path planning, and motion prediction. An importa…
OpenLiDARMap: Zero-Drift Point Cloud Mapping using Map Priors
Dominik Kulmer, Maximilian Leitenstern, Marcel Weinmann +1
Accurate localization is a critical component of mobile autonomous systems, especially in Global Navigation Satellite Systems (GNSS)-denied environments where traditional methods f…
Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration Framework
Dominik Kulmer, Ilir Tahiraj, Andrii Chumak +1
Today's autonomous vehicles rely on a multitude of sensors to perceive their environment. To improve the perception or create redundancy, the sensor's alignment relative to each ot…
Scenario Understanding of Traffic Scenes Through Large Visual Language Models
Esteban Rivera, Jannik Lübberstedt, Nico Uhlemann +1
Deep learning models for autonomous driving, encompassing perception, planning, and control, depend on vast datasets to achieve their high performance. However, their generalizatio…