22 citations · 31 across the 15 of their papers we have counts for
11 papers · 1 filter
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…
Multi-Resolution Voxelized Map-Based Stereo Visual-Inertial Odometry
Shuyi Pan, Hangtian Wang, Zhaoxing Zhang +3
Incorporating prior maps significantly enhances the accuracy and robustness of pose estimation in visual-inertial odometry (VIO). However, the large data volume of such maps, combi…
Event-based Stereo Visual-Inertial Odometry with Voxel Map
Zhaoxing Zhang, Xiaoxiang Wang, Chengliang Zhang +3
The event camera, renowned for its high dynamic range and exceptional temporal resolution, is recognized as an important sensor for visual odometry. However, the inherent noise in…
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…
Semi-Elastic LiDAR-Inertial Odometry
Zikang Yuan, Fengtian Lang, Tianle Xu +3
Existing LiDAR-inertial state estimation assumes that the state at the beginning of current sweep is identical to the state at the end of last sweep. However, if the state at the e…
LIWO: Lidar-Inertial-Wheel Odometry
Zikang Yuan, Fengtian Lang, Tianle Xu +1
LiDAR-inertial odometry (LIO), which fuses complementary information of a LiDAR and an Inertial Measurement Unit (IMU), is an attractive solution for state estimation. In LIO, both…
SR-LIO: LiDAR-Inertial Odometry with Sweep Reconstruction
Zikang Yuan, Fengtian Lang, Tianle Xu +1
This paper proposes a novel LiDAR-Inertial odometry (LIO), named SR-LIO, based on an iterated extended Kalman filter (iEKF) framework. We adapt the sweep reconstruction method, whi…