Kejian J. Wu

dblp:371/4946 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 since 2021Systems, architecture and hardware · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Robot navigation and mapping · 96% 3D vision · 4%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › state estimation
observability analysis
1.622025
Infield Self-Calibration of Intrinsic Parameters for Two Rigidly Connected IMUs · ICRA 2025
Dual-IMU State Estimation for Relative Localization of Two Mobile Agents · ICRA 2024
Robotics › Robot navigation and mapping
state estimation
1.622025
Infield Self-Calibration of Intrinsic Parameters for Two Rigidly Connected IMUs · ICRA 2025
Dual-IMU State Estimation for Relative Localization of Two Mobile Agents · ICRA 2024
Robotics › Robot navigation and mapping › sensor calibration
inertial measurement unit calibration
0.912025
Infield Self-Calibration of Intrinsic Parameters for Two Rigidly Connected IMUs · ICRA 2025
Robotics › Robot navigation and mapping › sensor calibration
IMU bias estimation
0.812024
Dual-IMU State Estimation for Relative Localization of Two Mobile Agents · ICRA 2024
Robotics › Robot navigation and mapping › localization
relative localization
0.812024
Dual-IMU State Estimation for Relative Localization of Two Mobile Agents · ICRA 2024
Computer vision › 3D vision › camera calibration
intrinsic parameter estimation
0.312025
Infield Self-Calibration of Intrinsic Parameters for Two Rigidly Connected IMUs · ICRA 2025

Methods — techniques the papers use, named apart from their topics

numerical simulation · 1.6observability analysis · 0.9extended kalman filter · 0.8
YearPublicationVenuePosition
2025 Infield Self-Calibration of Intrinsic Parameters for Two Rigidly Connected IMUs
abstract
This paper presents a study on the infield self-calibration of two rigidly connected IMUs' intrinsic parameters, without the aid of any external sensors, equipment, or specialized procedures. Specifically, we consider the calibration of gyroscope biases, gyroscope scale factors, and accelerometer biases, using only IMU data and known extrinsics between the two IMUs. We focus on the observability analysis of this system, and show that all gyroscope intrinsic parameters and a portion of accelerometer biases are observable, with information from both IMUs and sufficient motion. Moreover, we identify the additional unobservable directions in the intrinsic parameters that arise from various degenerate motions. Finally, we validate our observability findings through numerical simulations, and assess our system's calibration accuracy using real-world data.
Wenqian Lai, Ruonan Guo, Kejian J. Wu
ICRA4
2024 Dual-IMU State Estimation for Relative Localization of Two Mobile Agents
abstract
In this paper, we address the problem of relative localization of two mobile agents. Specifically, we consider the Dual-IMU system, where each agent is equipped with one IMU, and employs relative pose observations between them. Previous works, however, typically assumed known ego motion and ignored biases of the IMUs. Instead, we study the most general case of unknown biases for both IMUs. Besides the derivation of dynamic model equations of the proposed system, we focus on the observability analysis, for the observability under general motion and the unobservable directions arising from various special motions. Through numerical simulations, we validate our key observability findings and examine their impact on the estimation accuracy and consistency. Finally, the system is implemented to achieve effective relative localization of an HMD with respect to a vehicle moving in the real world.
Wenqian Lai, Ruonan Guo, Kejian J. Wu
ICRA3