6 papers
LinStereo: Linear-Complexity Global Attention for Multi-Scale Iterative Stereo Matching
Yiran Wang, Oliver Turner, Viorela Ila
Existing Vision Foundation Model (VFM)-based iterative stereo pipelines under-exploit three information pathways: multi-scale backbone features are collapsed into single-level corr…
DynoJEPP: Joint Estimation, Prediction and Planning in Dynamic Environments
Mikolaj Kliniewski, Jesse Morris, Yiduo Wang +2
DynoJEPP is a factor-graph-based framework that jointly formulates and simultaneously optimizes estimation, prediction, and planning in dynamic environments. In conventional factor…
DynoSAM: Open-Source Smoothing and Mapping Framework for Dynamic SLAM
Jesse Morris, Yiduo Wang, Mikolaj Kliniewski +1
Traditional Visual Simultaneous Localization and Mapping (vSLAM) systems focus solely on static scene structures, overlooking dynamic elements in the environment. Although effectiv…
Online Dynamic SLAM with Incremental Smoothing and Mapping
Jesse Morris, Yiduo Wang, Viorela Ila
Dynamic SLAM methods jointly estimate for the static and dynamic scene components, however existing approaches, while accurate, are computationally expensive and unsuitable for onl…
Bias-Eliminated PnP for Stereo Visual Odometry: Provably Consistent and Large-Scale Localization
Guangyang Zeng, Yuan Shen, Ziyang Hong +4
In this paper, we first present a bias-eliminated weighted (Bias-Eli-W) perspective-n-point (PnP) estimator for stereo visual odometry (VO) with provable consistency. Specifically,…
Training Trajectory Predictors Without Ground-Truth Data
Mikolaj Kliniewski, Jesse Morris, Ian R. Manchester +1
This paper presents a framework capable of accurately and smoothly estimating position, heading, and velocity. Using this high-quality input, we propose a system based on Trajectro…