Title: Modular Sensor Fusion for Semantic Segmentation,
Abstract: Sensor fusion is a fundamental process in robotic systems as it extends the
perceptual range and increases robustness in real-world operations. Current
multi-sensor deep learning based semantic segmentation approaches do not
provide robustness to under-performing classes in one modality, or require a
specific architecture with access to the full aligned multi-sensor training
data. In this work, we analyze statistical fusion approaches for semantic
segmentation that overcome these drawbacks while keeping a competitive
performance. The studied approaches are modular by construction, allowing to
have different training sets per modality and only a much smaller subset is
needed to calibrate the statistical models. We evaluate a range of statistical
fusion approaches and report their performance against state-of-the-art
baselines on both real-world and simulated data. In our experiments, the
approach improves performance in IoU over the best single modality segmentation
results by up to 5%. We make all implementations and configurations publicly
available.