18 citations · 32 across the 4 of their papers we have counts for
6 papers · 1 filter
NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation
Zhicheng Zhou, Cheng Zhao, Daniel Adolfsson +4
3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-challenged environments and serves as an essential component (i.e. loop-closure detection) in…
Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments
Li Sun, Daniel Adolfsson, Martin Magnusson +3
This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based loca…
EPANer Team Description Paper for World Robot Challenge 2020
Zhi Yan, Nathan Crombez, Li Sun
This paper presents the research focus and ideas incorporated in the EPANer robotics team, entering the World Robot Challenge 2020 - Partner Robot Challenge (Real Space).
EU Long-term Dataset with Multiple Sensors for Autonomous Driving
Zhi Yan, Li Sun, Tomas Krajnik +1
The field of autonomous driving has grown tremendously over the past few years, along with the rapid progress in sensor technology. One of the major purposes of using sensors is to…
Recurrent-OctoMap: Learning State-based Map Refinement for Long-Term Semantic Mapping with 3D-Lidar Data
Li Sun, Zhi Yan, Anestis Zaganidis +2
This paper presents a novel semantic mapping approach, Recurrent-OctoMap, learned from long-term 3D Lidar data. Most existing semantic mapping approaches focus on improving semanti…
Learning monocular visual odometry with dense 3D mapping from dense 3D flow
Cheng Zhao, Li Sun, Pulak Purkait +2
This paper introduces a fully deep learning approach to monocular SLAM, which can perform simultaneous localization using a neural network for learning visual odometry (L-VO) and d…