25 citations · 39 across the 13 of their papers we have counts for
7 papers · 1 filter
A2RL V\textsubscript{max}: The A2RL autonomous racing dataset for long-range, high-speed perception and multi-vehicle interaction
Marvin Klemp, Dominic Ebner, Cornelius Schröder +15
In autonomous driving development, a perception dataset is crucial, as it provides fundamental data for training, testing, and validating algorithms for an autonomous vehicle's mul…
Taming Perception Jitter: Uncertainty-Aware LiDAR Object Detection for Reliable Motion Classification
Cornelius Schröder, Žygimantas Marcinkus, Markus Lienkamp
Reliable motion classification is critical for autonomous driving, as false dynamic predictions of static objects can cascade into unnecessary planner interventions. Unstable bound…
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…
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…
GMMCalib: Extrinsic Calibration of LiDAR Sensors using GMM-based Joint Registration
Ilir Tahiraj, Felix Fent, Philipp Hafemann +2
State-of-the-art LiDAR calibration frameworks mainly use non-probabilistic registration methods such as Iterative Closest Point (ICP) and its variants. These methods suffer from bi…