activity
20162022
most citedFast, On-board, Model-aided Visual-Inertial Odometry System for Quadrotor Micro Aerial Vehicles

20 citations · 36 across the 12 of their papers we have counts for

collaborators

19 papers

cs.RO2022

Observability Analysis of Graph SLAM-Based Joint Calibration of Multiple Microphone Arrays and Sound Source Localization

Yuanzheng He, Jiang Wang, Daobilige Su +5

Multiple microphone arrays have many applications in robot audition, including sound source localization, audio scene perception and analysis, etc. However, accurate calibration of…

cs.RO2022

Map-based Visual-Inertial Localization: Consistency and Complexity

Zhuqing Zhang, Yanmei Jiao, Shoudong Huang +2

Drift-free localization is essential for autonomous vehicles. In this paper, we address the problem by proposing a filter-based framework, which integrates the visual-inertial odom…

cs.RO20221 cited

Toward Consistent and Efficient Map-based Visual-inertial Localization: Theory Framework and Filter Design

Zhuqing Zhang, Yang Song, Shoudong Huang +2

This paper focuses on designing a consistent and efficient filter for map-based visual-inertial localization. First, we propose a new Lie group with its algebra, based on which a n…

cs.RO20213 cited

A Right Invariant Extended Kalman Filter for Object based SLAM

Yang Song, Zhuqing Zhang, Jun Wu +3

With the recent advance of deep learning based object recognition and estimation, it is possible to consider object level SLAM where the pose of each object is estimated in the SLA…

cs.RO2021

Toward Consistent Drift-free Visual Inertial Localization on Keyframe Based Map

Zhuqing Zhang, Yanmei Jiao, Shoudong Huang +2

Global localization is essential for robots to perform further tasks like navigation. In this paper, we propose a new framework to perform global localization based on a filter-bas…

cs.RO2020

Globally optimal consensus maximization for robust visual inertial localization in point and line map

Yanmei Jiao, Yue Wang, Bo Fu +4

Map based visual inertial localization is a crucial step to reduce the drift in state estimation of mobile robots. The underlying problem for localization is to estimate the pose f…