Alignment Sets for Sensor Fusion Against Temporal Misalignment

DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

Abstract

Sensor fusion algorithms combine data from multiple sensors to produce more accurate and reliable results. However, temporal misalignment between sensors, caused by factors such as clock drift, jitter or networking delays, can significantly degrade fusion quality. Prior work on modeling temporal misalignment in sensor fusion algorithms assumes that in the ideal case all samples should be aligned with the same reference time point. We show that this assumption limits its applicability when samples are intentionally taken at different time points, e.g., when a single sensor is sampled multiple times or when sensors operate at different frequencies. In this paper, we introduce alignment sets, which allow system designers to explicitly specify the intended alignment between samples. This flexibility enables more precise temporal misalignment measures that better reflect the actual requirements of sensor fusion scenarios. We prove that alignment sets generalize the prior definitions of temporal misalignment of sensor fusion algorithms. We also provide an evaluation on a camera-LiDAR fusion pipeline for 3D object detection, showing that alignment sets provide more accurate misalignment measures and robustness estimates.

Authors 6

1. Daniel Kuhse

   TU Dortmund University

   Affiliation as printed

   TU Dortmund, Germany

2. Mario Günzel

   TU Dortmund University

   Affiliation as printed

   TU Dortmund, Germany

3. Harun Teper

   TU Dortmund University

   Affiliation as printed

   TU Dortmund, Germany

4. Lars Willemsen

   TU Dortmund University

   Affiliation as printed

   TU Dortmund, Germany

5. Georg von der Brüggen

   TU Dortmund University

   Affiliation as printed

   TU Dortmund, Germany

6. Jian-Jia Chen Aachen

   TU Dortmund University · RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Germany

   TU Dortmund, Germany