Hermann Blum, Abel Gawel, Roland Siegwart and Cesar Cadena
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018
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.
@inproceedings{blum2018fusion,
Title = {Modular Sensor Fusion for Semantic Segmentation},
Author = {Blum, H. and Gawel, A. and Siegwart, R. and Cadena, C.},
Fullauthor = {Blum, Hermann and Gawel, Abel and Siegwart, Roland and Cadena, Cesar},
Booktitle = {2018 {IEEE/RSJ} International Conference on Intelligent Robots and Systems ({IROS})},
Month = {October},
Year = {2018},
}