  The joint detection of drivable areas and road anomalies is a crucial task
for ground mobile robots. In recent years, many impressive semantic
segmentation networks, which can be used for pixel-level drivable area and road
anomaly detection, have been developed. However, the detection accuracy still
needs improvement. Therefore, we develop a novel module named the Normal
Inference Module (NIM), which can generate surface normal information from
dense depth images with high accuracy and efficiency. Our NIM can be deployed
in existing convolutional neural networks (CNNs) to refine the segmentation
performance. To evaluate the effectiveness and robustness of our NIM, we embed
it in twelve state-of-the-art CNNs. The experimental results illustrate that
our NIM can greatly improve the performance of the CNNs for drivable area and
road anomaly detection. Furthermore, our proposed NIM-RTFNet ranks 8th on the
KITTI road benchmark and exhibits a real-time inference speed.
