We propose Super Odometry, a high-precision multi-modal sensor fusion framework,
providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and …

This paper presents a hybrid ensemble classifier combined synthetic minority oversampling
technique (SMOTE), random search (RS) hyper-parameters optimization algorithm and …

Current state-of-the-art 3D scene understanding methods are merely designed in a full-
supervised way. However, in the limited reconstruction cases, only limited 3D scenes can be …

This work presents FG-Net, a general deep learning framework for large-scale point cloud
understanding without voxelizations, which achieves accurate and real-time performance …

LiDAR sensors have the advantage of being able to generate high-resolution imaging
quickly during both day and night; however, their performance is severely limited in adverse …

Many modern robotics applications rely on 3D maps of the environment. Due to the large
memory requirements of dense 3D maps, compression techniques are often necessary to …

Mapping an environment is essential for several robotic tasks, particularly for localization. In
this letter, we address the problem of mapping the environment using LiDAR point clouds …

Sidewalks are a critical infrastructure to facilitate essential daily trips for pedestrian and
wheelchair users. The dependence on the infrastructure and the increasing demand from …

Searching for an efficient and reliable method to reduce manual intervention and subjective
parameter selection during the discontinuity characterization process of rock tunnel faces is …

Existing state-of-the-art 3D point clouds understanding methods only perform well in a fully
supervised manner. To the best of our knowledge, there exists no unified framework which …