  Joint detection of drivable areas and road anomalies is very important for
mobile robots. Recently, many semantic segmentation approaches based on
convolutional neural networks (CNNs) have been proposed for pixel-wise drivable
area and road anomaly detection. In addition, some benchmark datasets, such as
KITTI and Cityscapes, have been widely used. However, the existing benchmarks
are mostly designed for self-driving cars. There lacks a benchmark for ground
mobile robots, such as robotic wheelchairs. Therefore, in this paper, we first
build a drivable area and road anomaly detection benchmark for ground mobile
robots, evaluating the existing state-of-the-art single-modal and data-fusion
semantic segmentation CNNs using six modalities of visual features.
Furthermore, we propose a novel module, referred to as the dynamic fusion
module (DFM), which can be easily deployed in existing data-fusion networks to
fuse different types of visual features effectively and efficiently. The
experimental results show that the transformed disparity image is the most
informative visual feature and the proposed DFM-RTFNet outperforms the
state-of-the-arts. Additionally, our DFM-RTFNet achieves competitive
performance on the KITTI road benchmark. Our benchmark is publicly available at
https://sites.google.com/view/gmrb.
