A Dynamic Object Removal and Reconstruction Algorithm for Point Clouds
Sarat Chandra Nagavarapu (Agency for Science, Technology and Research )
Anuj Abraham (Technology Innovation Institute)
Nithish Muthuchamy Selvaraj (Nanyang Technological University)
Justin Dauwels (TU Delft - Signal Processing Systems)
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Abstract
Autonomous vehicles (AV) are one of the greatest technological advancements of this decade and a giant leap in the transportation industry and mobile robotics. Autonomous vehicles face several major challenges in achieving higher levels of autonomy. One of these is to find a fast and reliable algorithm to process the sensor data so that the simultaneous localization and mapping (SLAM) algorithms run in real-time to achieve autonomous navigation. The major limitation of the SLAM algorithm, especially while building a map is to have static environmental features, i.e. without any dynamic or moving objects. To achieve this, our paper introduces a novel algorithm to remove dynamic objects from point cloud data. The algorithm focuses on identifying and removing dynamic objects from sensor data, thereby creating a static scene suitable for traditional SLAM algorithms. Simulations conducted on the benchmark dataset demonstrate the algorithm's efficacy in successfully eliminating dynamic objects and reconstructing a stable static scene.