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Airborne particle classification in LiDAR point clouds using deep learning
(2021)Springer Proceedings in Advanced Robotics ~ Field and Service RoboticsLiDAR sensors have been very popular in robotics due to their ability to provide accurate range measurements and their robustness to lighting conditions. However, their sensitivity to airborne particles such as dust or fog can lead to perception algorithm failures (e.g., the detection of false obstacles by field robots). In this work, we address this problem by proposing methods to classify airborne particles in LiDAR data. We propose and ...Conference Paper -
Object Classification Based on Unsupervised Learned Multi-Modal Features For Overcoming Sensor Failures
(2019)2019 International Conference on Robotics and Automation (ICRA)Conference Paper -
3D Ground Point Classification for Automotive Scenarios
(2018)2018 21st International Conference on Intelligent Transportation Systems (ITSC)Autonomous driving applications must be provided with information about other road users and road side infrastructure by object detection modules. These modules often process point clouds sensed by light detection and ranging (LiDAR) sensors. Within the captured point cloud a large amount of points correspond to physical locations on the ground. These points do not hold information about road users, obstacles or road side infrastructure. ...Conference Paper