论文标题
在可驱动区域和道路异常检测中应用表面正常信息，以供地面移动机器人
Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots
论文作者
论文摘要
对于地面移动机器人来说，对可驱动区域和道路异常的联合检测是至关重要的任务。近年来，已经开发了许多令人印象深刻的语义分割网络，这些网络可用于像素级可驱动区域和道路异常检测。但是，检测准确性仍然需要提高。因此，我们开发了一个名为“正常推理”模块（NIM）的新型模块，该模块可以以高精度和效率从密集的深度图像中产生表面正常信息。我们的NIM可以部署在现有的卷积神经网络（CNN）中，以完善细分性能。为了评估NIM的有效性和鲁棒性，我们将其嵌入了十二个最先进的CNN中。实验结果表明，我们的NIM可以大大提高CNN的性能，以驱动区域和道路异常检测。此外，我们提议的NIM-RTFNET在Kitti Road基准测试中排名第八，并具有实时推理速度。
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.
