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Markus Lienkamp

4 accepted papers

2026

Drifting in the Future: Stabilizing Path Following Drifting on High-Latency Vehicle Systems

ICRA 2026poster

Autonomously controlling and handling a vehicle at and beyond its stability limit is a mathematically and computationally demanding task. Prior demonstrations of automated drifting have been limited to research platforms with instantaneous torque delivery and independently actuated wheels, leaving t…

2025

Cal or No Cal? - Real-Time Miscalibration Detection of LiDAR and Camera Sensors

IROS 2025

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 perspective, sensor calibration is a key enabler of autonomous driving. In the current state of the art, a trend from target

Cited by 2SourcecodeScholar
2024

GMMCalib: Extrinsic Calibration of LiDAR Sensors using GMM-based Joint Registration

IROS 2024poster

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 biased results due to their pair-wise registration procedure as well as their sensitivity to initialization and parameteri…

Cited by 4SourcecodeScholar
2024

MAN TruckScenes: A multimodal dataset for autonomous trucking in diverse conditions

NeurIPS 2024poster

Autonomous trucking is a promising technology that can greatly impact modern logistics and the environment. Ensuring its safety on public roads is one of the main duties that requires an accurate perception of the environment. To achieve this, machine learning methods rely on large datasets, but to…

Cited by 4SourcePDFScholar