9 papers
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…
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)…
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…
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…