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[R] Autonomous Navigation in Unconstrained Environments

While several datasets for autonomous navigation have become available in recent years, they have tended to focus on structured driving environments. This usually corresponds to well-delineated infrastructure such as lanes, a small number of well-defined categories for traffic participants, low variation in an object or background appearance and strong adherence to traffic rules.

I recently worked with IDD, dataset collected from India. It’s relatively more challenging than other autonomous navigation-related datasets (such as Berkeley deep drive or cityscapes) since much of the data has been captured from non standard conditions (drivable areas except roads etc.).

I’m releasing the code for this work, feel free to use it for your projects or research.

Github: https://github.com/prajjwal1/autonomous-object-detection

Dataset: https://idd.insaan.iiit.ac.in/

submitted by /u/vector_machines
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.