activity
20172023
most citedCubeSLAM: Monocular 3D Object SLAM

452 citations · 1.8k across the 109 of their papers we have counts for

collaborators
Showing 2022Show all

30 papers · 1 filter

cs.CV2022★ 1 cited

Attention-Enhanced Cross-modal Localization Between 360 Images and Point Clouds

Zhipeng Zhao, Huai Yu, Chenwei Lyv +2

Visual localization plays an important role for intelligent robots and autonomous driving, especially when the accuracy of GNSS is unreliable. Recently, camera localization in LiDA…

cs.RO2022★ 4 cited

UAS Simulator for Modeling, Analysis and Control in Free Flight and Physical Interaction

Azarakhsh Keipour, Mohammadreza Mousaei, Dongwei Bai +2

This paper presents the ARCAD simulator for the rapid development of Unmanned Aerial Systems (UAS), including underactuated and fully-actuated multirotors, fixed-wing aircraft, and…

cs.RO2022★ 6 cited

Challenges in Close-Proximity Safe and Seamless Operation of Manned and Unmanned Aircraft in Shared Airspace

Jay Patrikar, Joao P. A. Dantas, Sourish Ghosh +11

We propose developing an integrated system to keep autonomous unmanned aircraft safely separated and behave as expected in conjunction with manned traffic. The main goal is to achi…

cs.RO2022

When Geometry is not Enough: Using Reflector Markers in Lidar SLAM

Gerhard Kurz, Sebastian A. Scherer, Peter Biber +1

Lidar-based SLAM systems perform well in a wide range of circumstances by relying on the geometry of the environment. However, even mature and reliable approaches struggle when the…

cs.CV2022

PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework

Bowen Li, Ziyuan Huang, Junjie Ye +4

Visual object tracking is essential to intelligent robots. Most existing approaches have ignored the online latency that can cause severe performance degradation during real-world…

cs.CV2022★ 8 cited

Pseudo-Label Noise Suppression Techniques for Semi-Supervised Semantic Segmentation

Sebastian Scherer, Robin Schön, Rainer Lienhart

Semi-supervised learning (SSL) can reduce the need for large labelled datasets by incorporating unlabelled data into the training. This is particularly interesting for semantic seg…