Traversability mapping in off-road environment using semantic segmentation

Autonomous driving in off-road environments is challenging as it does not have a definite terrain structure. Assessment of terrain traversability is the main factor in deciding the autonomous driving capability of the ground vehicle. Traversability in off-road environments is defined as the drivable track on the trails by different vehicles used in autonomous driving. It is very crucial for the autonomous ground vehicle (AGV) to avoid obstacles such as trees, boulders etc. while traversing through the trails. The goal of this research has three main objectives: a) collection of 2D camera data in the off-road / unstructured environment, b) annotation of 2D camera data depending on the vehicles’ ability to drive through the trails , and c) application of semantic segmentation algorithm on the labeled dataset to predict the trajectory based on the type of ground vehicle. Our models and labeled datasets will be publicly available.

[1]  George L. Mason,et al.  Determining forces required to override obstacles for ground vehicles , 2012 .

[2]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  M. Trivedi,et al.  Off-Road Terrain Traversability Analysis and Hazard Avoidance for UGVs , 2011 .

[5]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[7]  Srikanth Saripalli,et al.  RELLIS-3D Dataset: Data, Benchmarks and Analysis , 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

[8]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Sebastian Scherer,et al.  Real-Time Semantic Mapping for Autonomous Off-Road Navigation , 2017, FSR.

[10]  Christopher Goodin,et al.  LiDAR Data Segmentation in Off-Road Environment Using Convolutional Neural Networks (CNN) , 2020 .

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.