22 citations · 31 across the 15 of their papers we have counts for
16 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…
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…
Towards Secure and Efficient DNN Accelerators via Hardware-Software Co-Design
Wei Xuan, Zihao Xuan, Rongliang Fu +8
The rapid deployment of deep neural network (DNN) accelerators in safety-critical domains such as autonomous vehicles, healthcare systems, and financial infrastructure necessitates…
An adaptive adjoint-oriented neural network for solving parametric optimal control problems with singularities
Zikang Yuan, Guanjie Wang, Qifeng Liao
In this work, we present an adaptive adjoint-oriented neural network (adaptive AONN) for solving parametric optimal control problems governed by partial differential equations. The…
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)…
MonSter++: Unified Stereo Matching, Multi-view Stereo, and Real-time Stereo with Monodepth Priors
Junda Cheng, Wenjing Liao, Zhipeng Cai +10
We introduce MonSter++, a geometric foundation model for multi-view depth estimation, unifying rectified stereo matching and unrectified multi-view stereo. Both tasks fundamentally…
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…
SR-LIVO: LiDAR-Inertial-Visual Odometry and Mapping with Sweep Reconstruction
Zikang Yuan, Jie Deng, Ruiye Ming +2
Existing LiDAR-inertial-visual odometry and mapping (LIV-SLAM) systems mainly utilize the LiDAR-inertial odometry (LIO) module for structure reconstruction and the visual-inertial…
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…