MapLite: Autonomous intersection navigation without detailed prior maps

A Toyota Prius used in real-world runs of the MapLite system

Abstract

In this work, we present MapLite- a one-click autonomous navigation system capable of piloting a vehicle to an arbitrary desired destination point given only a sparse publicly available topometric map (from OpenStreetMap). The onboard sensors are used to segment the road region and register the topometric map in order to fuse the high-level navigation goals with a variational path planner in the vehicle frame. This enables the system to plan trajectories that correctly navigate road intersections without the use of an external localization system such as GPS or a detailed prior map. Since the topometric maps already exist for the vast majority of roads, this solution greatly increases the geographical scope for autonomous mobility solutions. We implement MapLite on a full-scale autonomous vehicle and exhaustively test it on over 15 km of road including over 100 autonomous intersection traversals. We further extend these results through simulated testing to validate the system on complex road junction topologies such as traffic circles.

Publication
In Robotics and Automation Letters
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Teddy Ort
PhD Student
Krishna Murthy Jatavallabhula
Krishna Murthy Jatavallabhula
PhD Candidate

My research blends robotics, computer vision, graphics, and physics with deep learning.

Sai Krishna G.V.
Masters Student
Dhaivat Bhatt
Masters Student
Igor Gilitschenski
Assistant Professor
Liam Paull
Assistant Professor

I lead the Montreal robotics and embodied AI lab. I am affiliated with Université de Montréal, Mila, and I hold a CIFAR AI chair.

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