Shasa Antao

Pittsburgh, Pennsylvania, United States Contact Info
356 followers 344 connections

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About

Experienced perception engineer in the Mobile Robotics field. Graduated from the Robotic…

Activity

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Experience & Education

  • Onward Robotics

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Licenses & Certifications

Courses

  • Computer Graphics

    15-662

  • Computer Vision

    16-720B

  • Geometry based Vision

    16-887

  • Machine Learning

    10-601

  • Robot Autonomy

    16-662

  • Robot Localization & Mapping (SLAM)

    16-833A

  • Robot Mobility on Air, Land & Sea

    16-665

  • Visual Recognition and Learning

    16-824

Projects

  • Experimenting with Low-Resolution Point Clouds and One-Shot Learning for 3D Object Detection

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    One of the major challenges in developing a point cloud based object detection system stems from the fact that point cloud data is irregular, unordered, and usually sparse. Another challenge in using these detection systems for novel applications is that there is a limited amount of accurately labeled class data. One-shot learning can solve the problem of having a dataset with few samples per class, and in this project, we wanted to try and extrapolate concepts from a state-of-the-art one-shot…

    One of the major challenges in developing a point cloud based object detection system stems from the fact that point cloud data is irregular, unordered, and usually sparse. Another challenge in using these detection systems for novel applications is that there is a limited amount of accurately labeled class data. One-shot learning can solve the problem of having a dataset with few samples per class, and in this project, we wanted to try and extrapolate concepts from a state-of-the-art one-shot learner for 2D RGB object detection and try applying these concepts to perform object detection on 3D point cloud data. .

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  • Autonomous Driving for Adverse Perceived Terrain

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    Research suggests that thousands of people are killed, hundreds of thousands are injured, and billions of dollars are wasted, every year during harsh adverse weather events. Even in the event that a high level of autonomy is achieved for autonomous vehicles in dry and sunny conditions, it will likely not translate to any reduction in these numbers, since people will always need to travel and transport goods, no matter the state of the road conditions.

    The ADAPT (Autonomous Driving for…

    Research suggests that thousands of people are killed, hundreds of thousands are injured, and billions of dollars are wasted, every year during harsh adverse weather events. Even in the event that a high level of autonomy is achieved for autonomous vehicles in dry and sunny conditions, it will likely not translate to any reduction in these numbers, since people will always need to travel and transport goods, no matter the state of the road conditions.

    The ADAPT (Autonomous Driving for Adverse Perceived Terrain) system is designed to combat this issue. The system serves to actively detect changes in road conditions and adjust the vehicle controls to account for these changes, making transitions into wet or otherwise unfavorable road conditions a much safer endeavor. Current systems are almost exclusively reactive, meaning that they only modify the vehicle’s actions after an incident has occurred, whereas the ADAPT system aims to predict future scenarios and maximize the ability to safely transport passengers and goods.

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