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Automatic Sensor Placement for Model-based Robot Vision
Automatic Sensor Placement for Model-based Robot Vision
Panda robot with custom object in Gazebo
Panda robot with custom object in Gazebo
Segmented object with PCL
Segmented object with PCL
Feature matching with PCL
Feature matching with PCL
3D reconstruction of object for comparison application
3D reconstruction of object for comparison application
Description
Description
This project is a part of Autonomy, Controls, and Estimation Lab at WPI. The goal of the project is to generate a view planner to scan an object with minimum number of viewpoints. We implement the algorithm developed in this paper using ROS, Gazebo, and PCL. This was a funded project and no source code can be shared online.
This project is a part of Autonomy, Controls, and Estimation Lab at WPI. The goal of the project is to generate a view planner to scan an object with minimum number of viewpoints. We implement the algorithm developed in this paper using ROS, Gazebo, and PCL. This was a funded project and no source code can be shared online.
My Role
My Role
• Setup a simulation environment with a depth camera on the Panda robot in Gazebo
• Setup a simulation environment with a depth camera on the Panda robot in Gazebo
• Generated C++ nodes for filtering, segmentation, and generating bounding box for pointcloud clusters using PCL
• Generated C++ nodes for filtering, segmentation, and generating bounding box for pointcloud clusters using PCL
• Generated Python scripts for position control of the robot
• Generated Python scripts for position control of the robot
• Implemented the algoithm in MATLAB for tuning parameters and testing various fitness functions
• Implemented the algoithm in MATLAB for tuning parameters and testing various fitness functions
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