Multi-modal systems have the capacity of producing more reliable results than systems with a single modality in road detection due to perceiving different aspects of the scene. We focus on using raw sensor inputs instead of, as it is typically done in many SOTA works, leveraging architectures that require high pre-processing costs such as surface normals or dense depth predictions. By using raw sensor inputs, we aim to utilize a low-cost model thatminimizes both the pre-processing andmodel computation costs. This study presents a cost-effective and highly accurate solution for road segmentation by integrating data from multiple sensorswithin a multi-task learning architecture.Afusion architecture is proposed in which RGB and LiDAR depth images constitute the inputs of the network. Another contribution of this study is to use IMU/GNSS (inertial measurement unit/global navigation satellite system) inertial navigation system whose data is collected synchronously and calibrated with a LiDAR-camera to compute aggregated dense LiDAR depth images. It has been demonstrated by experiments on the KITTI dataset that the proposed method offers fast and high-performance solutions. We have also shown the performance of our method on Cityscapes where raw LiDAR data is not available. The segmentation results obtained for both full and half resolution images are competitive with existing methods. Therefore, we conclude that our method is not dependent only on raw LiDAR data; rather, it can be used with different sensor modalities. The inference times obtained in all experiments are very promising for real-time experiments.

翻译：多模态系统通过感知场景的不同方面，在道路检测中能够产生比单模态系统更可靠的结果。本研究聚焦于直接使用原始传感器输入，而非像当前许多先进工作中那样，采用需要高预处理成本（如表面法线或稠密深度预测）的架构。通过使用原始传感器输入，我们旨在利用一种低成本的模型，该模型能够同时降低预处理和模型计算成本。本研究提出了一种经济高效且高精度的道路分割解决方案，通过在多任务学习架构中融合多个传感器的数据。我们设计了一种融合架构，其中RGB图像和LiDAR深度图像构成网络的输入。本研究的另一贡献在于使用了IMU/GNSS（惯性测量单元/全球导航卫星系统）惯性导航系统，其数据通过LiDAR-相机进行同步采集与标定，用于计算聚合的稠密LiDAR深度图像。在KITTI数据集上的实验表明，所提方法能够提供快速且高性能的解决方案。我们还展示了该方法在无法获取原始LiDAR数据的Cityscapes数据集上的性能。在全分辨率和半分辨率图像上获得的分割结果均与现有方法具有竞争力。因此，我们得出结论：该方法不仅依赖于原始LiDAR数据，还可与不同的传感器模态配合使用。所有实验获得的推理时间对于实时应用而言极具前景。