Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps
By T. Ort, Liam Paull, Daniela Rus. 2018
131 citations recorded by the source corpus.
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.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
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Citations in the corpus
- Learning to Drive in a Day (2018, neutral): “Recent work in this area has demonstrated that this is possible on rural country roads, using GPS for coarse localisation and LIDAR to understand the local scene [2].”
- LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis (2018, methodology): “p-closure tasks, which are crucial for robotics and self-driving applications. 1. Introduction Autonomous navigation is paramount significant in robotic community such as helping self-driving vehicles [12] and unmanned aerial vehicles [21] achieve full autonomy. A common framework for an intelligent ”