4 papers
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…
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…