Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps
Name
ICRA2018_AutonomousVehicleNavigationRuralEnvironment.pdf
Description
Accepted version
Size
562.41 KB
Format
Adobe PDF
Checksum (MD5)
37da7b48323e56a9a014bad2a314f833
Author(s)
Ort, Teddy
Paull, Liam
Rus, Daniela
Date Issued
May 2018
Publisher
IEEE
Citation
Ort, Teddy, Paull, Liam and Rus, Daniela. 2018. "Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps."
Version
Author's final manuscript
Abstract
© 2018 IEEE. State-of-the-art autonomous driving systems rely heavily on detailed and highly accurate prior maps. However, outside of small urban areas, it is very challenging to build, store, and transmit detailed maps since the spatial scales are so large. Furthermore, maintaining detailed maps of large rural areas can be impracticable due to the rapid rate at which these environments can change. This is a significant limitation for the widespread applicability of autonomous driving technology, which has the potential for an incredibly positive societal impact. In this paper, we address the problem of autonomous navigation in rural environments through a novel mapless driving framework that combines sparse topological maps for global navigation with a sensor-based perception system for local navigation. First, a local navigation goal within the sensor view of the vehicle is chosen as a waypoint leading towards the global goal. Next, the local perception system generates a feasible trajectory in the vehicle frame to reach the waypoint while abiding by the rules of the road for the segment being traversed. These trajectories are updated to remain in the local frame using the vehicle's odometry and the associated uncertainty based on the least-squares residual and a recursive filtering approach, which allows the vehicle to navigate road networks reliably, and at high speed, without detailed prior maps. We demonstrate the performance of the system on a full-scale autonomous vehicle navigating in a challenging rural environment and benchmark the system on a large amount of collected data.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2018.8460519
