1 citations · 2 across the 7 of their papers we have counts for
8 papers
RAEM: Robust Autonomous Exploration for Multi-Floor Environments with a Quadruped Robot
Zikang Yuan, Yuan Ren, Yian Wang +10
In this paper, we propose RAEM, a robust autonomous exploration framework for quadruped robots operating in multi-floor environments. Most existing ground-robot exploration approac…
UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References
Ming-Feng Li, Xin Yang, Fu-En Wang +5
6D object pose estimation has shown strong generalizability to novel objects. However, existing methods often require either a complete, well-reconstructed 3D model or numerous ref…
Uni-Gaussians: Unifying Camera and Lidar Simulation with Gaussians for Dynamic Driving Scenarios
Zikang Yuan, Yuechuan Pu, Hongcheng Luo +7
Ensuring the safety of autonomous vehicles necessitates comprehensive simulation of multi-sensor data, encompassing inputs from both cameras and LiDAR sensors, across various dynam…
Direct Sparse Odometry with Continuous 3D Gaussian Maps for Indoor Environments
Jie Deng, Fengtian Lang, Zikang Yuan +1
Accurate localization is essential for robotics and augmented reality applications such as autonomous navigation. Vision-based methods combining prior maps aim to integrate LiDAR-l…
SR-LIO++: LiDAR-Inertial Odometry and Quantized Mapping with Caching-Aware Sweep Reconstruction
Zikang Yuan, Ruiye Ming, Chengwei Zhao +6
Addressing the inherent low acquisition frequency limitation of 3D LiDAR to achieve high-frequency output has become a critical research focus in the LiDAR-Inertial Odometry (LIO)…
Panoramic Direct LiDAR-assisted Visual Odometry
Qirui Hu, Zikang Yuan, Tianle Xu +3
Enhancing visual odometry by exploiting sparse depth measurements from LiDAR is a promising solution for improving tracking accuracy of an odometry. Most existing works utilize a m…
LiDAR-Inertial Odometry in Dynamic Driving Scenarios using Label Consistency Detection
Zikang Yuan, Xiaoxiang Wang, Jingying Wu +2
In this paper, a LiDAR-inertial odometry (LIO) method that eliminates the influence of moving objects in dynamic driving scenarios is proposed. This method constructs binarized lab…
IFTD: Image Feature Triangle Descriptor for Loop Detection in Driving Scenes
Fengtian Lang, Ruiye Ming, Zikang Yuan +1
In this work, we propose a fast and robust Image Feature Triangle Descriptor (IFTD) based on the STD method, aimed at improving the efficiency and accuracy of place recognition in…