3 accepted papers
Perception plays a crucial role in various robot applications. However existing well-annotated datasets are biased towards autonomous driving scenarios while unlabelled SLAM datasets are quickly over-fitted and often lack environment and domain variations. To expand the frontier of these fields we i…
In this study, we address a gap in existing unsupervised domain adaptation approaches on LiDAR-based 3D object detection, which have predominantly concentrated on adapting between established, high-density autonomous driving datasets. We focus on sparser point clouds, capturing scenarios from differ
Multimodal sensor fusion methods for 3D object detection have been revolutionizing the autonomous driving research field. Nevertheless, most of these methods heavily rely on dense LiDAR data and accurately calibrated sensors which is often not the case in real-world scenarios. Data from LiDAR and ca