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3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate pointlevel labels from a computer game. To our best knowledge, this is the first publication on LiDAR point cloud simulation framework for autonomous driving. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic registration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed.
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ADAPTIVE ACQUISITION OF AIRBORNE LIDAR POINT CLOUD BASED ON DEEP REINFORCEMENT LEARNING
Human experience involvement in existing operations of airborne Light Detection and Ranging (LIDAR) systems and off-line processing of collected LIDAR data make the acquisition process of airborne LIDAR point cloud less adaptable to environment conditions. This work develops a deep reinforcement learning-enabled framework for adaptive airborne LIDAR point cloud acquisition. Namely, the optimization of the airborne LIDAR operation is modeled as a Markov decision process (MDP). A set of LIDAR point cloud processing methods are proposed to derive the state space, action space, and reward function of the MDP model. A DRL algorithm, Deep Q-Network (DQN), is used to solve the MDP. The DRL model is trained in a flexible virtual environment by using simulator AirSim. Extensive simulation demonstrates the efficiency of the proposed framework.
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- Award ID(s):
- 1924278
- PAR ID:
- 10357308
- Date Published:
- Journal Name:
- 2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)
- Page Range / eLocation ID:
- 366 to 371
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MIPR54900.2022.00072
