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.

该研究提出了一种成本效益高且精确的道路分割解决方案，通过在多任务学习架构中整合来自多个传感器的数据，利用多模态系统比单模态系统能够产生更可靠的结果，使用原始传感器输入而非高预处理成本的架构，同时将 RGB 和 LiDAR 深度图像作为网络的输入，与 IMU/GNSS 惯性导航系统数据进行聚合，实验证明该方法在 KITTI 数据集和 Cityscapes 数据集上都取得了快速且高性能的解决方案，可以与不同的传感器模态一起使用，推理时间非常适合实时实验。