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Power-Line Dataset for Autonomous Drone Inspection
High-quality Power-line dataset for Autonomous Drone Inspection (PADI).
Keywords: Sensor fusion, perception
Classical power line inspection and maintenance are dangerous, costly and time consuming. Drones could mitigate the risk for humans and minimize the cost for direct benefit of the infrastructure. Several sensing capabilities has been already tested (i.e. RGB, LiDAR) which gives the drone the abilities to operate in unstructured environments.
Sensor fusion is a popular technique to get the best of each sensor for autonomous navigation. Benchmark of perception strategies is a key part for solid and robust algorithm development before final deployment on the system. However, the lack of relevant and accurate data for multiple sensors makes difficult the Verification and Validation (V&V) process of perception algorithms. The goals of this project is to deliver the first multi sensor power-line inspection dataset for drones with alternative sensory data and ground truth.
Requirements: Background in robotics and autonomous systems – Drone navigation preferable – Excellent programming in C++ and Python – Knowledge of ROS and robotic middle-ware - Passionate about robotics and engineering in general. - Linux
Classical power line inspection and maintenance are dangerous, costly and time consuming. Drones could mitigate the risk for humans and minimize the cost for direct benefit of the infrastructure. Several sensing capabilities has been already tested (i.e. RGB, LiDAR) which gives the drone the abilities to operate in unstructured environments.
Sensor fusion is a popular technique to get the best of each sensor for autonomous navigation. Benchmark of perception strategies is a key part for solid and robust algorithm development before final deployment on the system. However, the lack of relevant and accurate data for multiple sensors makes difficult the Verification and Validation (V&V) process of perception algorithms. The goals of this project is to deliver the first multi sensor power-line inspection dataset for drones with alternative sensory data and ground truth.
Requirements: Background in robotics and autonomous systems – Drone navigation preferable – Excellent programming in C++ and Python – Knowledge of ROS and robotic middle-ware - Passionate about robotics and engineering in general. - Linux
Release an open-access dataset for the evaluation of perception pipelines for autonomous drones. The goal is to establish a solid benchmark for autonomous drone inspection of power-lines . The following sensors are consider to be part of the dataset:
- Absolute depth information
- RGB images
- Event-based camera
- Thermography
- Inertial Sensory information
- Ground truth positioning
Release an open-access dataset for the evaluation of perception pipelines for autonomous drones. The goal is to establish a solid benchmark for autonomous drone inspection of power-lines . The following sensors are consider to be part of the dataset:
- Absolute depth information - RGB images - Event-based camera - Thermography - Inertial Sensory information - Ground truth positioning
Javier Hidalgo-Carrió (jhidalgocarrio@ifi.uzh.ch) and Giovanni Cioffi (cioffi@ifi.uzh.ch)
Javier Hidalgo-Carrió (jhidalgocarrio@ifi.uzh.ch) and Giovanni Cioffi (cioffi@ifi.uzh.ch)