LiDAR Fusion
LiDAR fusion integrates data from LiDAR sensors with other modalities, such as cameras, radar, and inertial measurement units (IMUs), to improve the accuracy, robustness, and completeness of environmental perception for applications like autonomous driving and robotics. Current research emphasizes efficient fusion techniques, often employing transformer-based architectures or Kalman filters, to handle the inherent differences in data structure and density between sensor types. This work aims to overcome limitations of individual sensors, particularly in challenging conditions like adverse weather or low light, leading to more reliable and safer autonomous systems.
Papers
September 21, 2024
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December 14, 2021
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