Multi-Modal Multi-Task (3MT) Road Segmentation
Erkan Milli Özgür Erkent Asım Egemen Yılmaz
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.