22 citations · 28 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…