Overall development and testing of the autonomous vehicle software framework.
Development of a LIDAR-based vehicle auto-localization system that detects and uses road features (curb and road marking) to obtain accurate position estimation.
Laser sensor based environment mapping.
Obstacle detection and classification (car, wall, pedestrian and tree) based on LIDAR data and machine learning techniques.
Road features detection (curbs and horizontal markings) based on LIDAR data and machine learning techniques.
ROS (Robot Operating Systems), PCL (Point Cloud Library), OpenCV (Computer Vision).
Detection the navigable areas for an autonomous tractor to work in orange crops using stereo cameras.
Development of methods for environment perception, such as road detection, dynamic/static obstacle detection, segmentation and tracking.
Sensor data calibration.
Build framework for point cloud data manipulation and evaluation of the methods.
Technology Architecture
Requirement Specification, Use Cases, Functional Specification, Technical Specification, Component and Integrated Tests.
Business Process Management (BPM).
Service Bus (SB).
Service Oriented Architecture (SOA).
Project Title: Localization for Autonomous Vehicle in Urban Environments Using Continuous Occupancy Maps.
Advisor: Denis Fernando Wolf.
Research on Gaussian Process applied to robot and vehicle mapping and localization.
Advisor: Fabio Tozeto Ramos.
Dissertation: Outdoor Mapping Using Mobile Robots.
Advisor: Denis Fernando Wolf.
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