Abstract Knowledge about the own pose is key for all mobile robot applications. Thus pose
estimation is part of the core functionalities of mobile robots. Over the last two decades …

Diffusion generative modeling has become a promising approach for learning robotic
manipulation tasks from stochastic human demonstrations. In this paper we present …

Deep neural networks (DNNs) in 3D scenes show a strong capability of extracting high-level
semantic features and significantly promote research in the 3D field. 3D shapes and scenes …

Point cloud registration is a foundational task for 3D alignment and reconstruction
applications. While both traditional and learning-based registration approaches have …

Knowledge about the own pose is key for all mobile robot applications. Thus pose
estimation is part of the core functionalities of mobile robots. In the last two decades, LiDAR …

Partial point cloud registration is a challenging problem in robotics, especially when the
robot undergoes a large transformation, causing a significant initial pose error and a low …

Loop closing is a core module of simultaneous localization and mapping (SLAM) in robotics.
It can be achieved through different sensors, of which LiDAR is dominant in large-scale …

This paper presents MinkUNeXt, an effective and efficient architecture for place-recognition
from point clouds entirely based on the new 3D MinkNeXt Block, a residual block composed …

This paper proposes an equivariant neural network that takes data in any semi-simple Lie
algebra as input. The corresponding group acts on the Lie algebra as adjoint operations …

Despite the emergence of various LiDAR-based place recognition methods, the challenge of
place recognition failure due to rotation remains critical. Existing studies have attempted to …