Robust Incremental SLAM Under Constrained Optimization Formulation

Bai, Fengshan; Vidal-Calleja, Teresa; Huang, Shoudong

doi:10.1109/lra.2018.2794610

Cited by 29 publications

(39 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, filtering methods lack in terms of dynamic behavior and the algorithm performance varies with the change of state matrixes [38,39]. They can somewhat slow down the error accumulation process, but not eliminate it completely [40,41]. With the booming of artificial intelligence, deep neural networks [42] have been applied in the fusion of multi-sensors, while the requirement of a large scale of data still remains a big challenge in practical applications.…”

Section: Sensor Fusion and Filteringmentioning

confidence: 99%

Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Zhang

et al. 2019

Electronics

View full text Add to dashboard Cite

Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors and drift. In this paper, we propose a multi-sensor hybrid method to solve this problem. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. Secondly, Cramér-Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Furthermore, proposed method is verified in 3D practical application scenarios. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications.

show abstract

Section: Sensor Fusion and Filteringmentioning

confidence: 99%

Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Zhang

et al. 2019

Electronics

View full text Add to dashboard Cite

show abstract

“…To further verify the computational performance, we implemented proposed method and compared it with state-of-thearts, namely SQP [20] and Active-set [34]. We set up a simulation test-field with four base stations (Anchors) whose coordinates are respectively (0, 0), (100, 0), (100, 100) and (0, 100).…”

Section: Computational Complexity Evaluationmentioning

confidence: 99%

“…4-(b), was attached to the experimenter's right ankle. In addition to above mentioned ''IMU'' and ''Kalman filtering'' comparative algorithms, two state-of-the-art optimization-based methods, i.e., SQP [20] and activeset [34], are also taken into consideration. For better comparison, ground truth and tracking trajectories with applying all mentioned methods (the proposed method and four comparative ones) are drawn in Fig.…”

Section: Practical Use Case In 3d Scenariomentioning

confidence: 99%

“…However, filtering methods lack in terms of dynamic behavior and the algorithm performance varies with the change of state matrix [11], [12]. They can to some extent slow down the error accumulation process, but not eliminate it completely [20], [34]. With the booming of artificial intelligence, deep neural networks [13] have been applied in the fusion of multi-sensors, while the requirement of a large scale of data still remains a big challenge in practical applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Estimation and Fundamental Limits for Target Localization Using IMU/TOA Fusion Method

Xu¹,

He²,

Li³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Localization is one of the most important topics of the cyber-physical system. In the last decades, much attention has been paid to the precise localization and performance evaluation in wireless sensor networks. The inertial measurement unit (IMU) and time-of-arrival (TOA) fusion is a state-of-theart method to solve the accumulative error and drifting problem faced by the sole IMU positioning and navigation method. Many of the existing studies are based on optimization. However, they usually face problems of non-convexity of the objective function, falling into local optimum, and the requirements for the prior/posterior probability distribution of measured values. All these reasons limit its practical applications toward accurate target tracking. This paper presents a Chebyshev-center-based optimization method. Geometrically considering the real position of the target, it aims at improving the target tracking accuracy. Cramér-Rao lower bound (CRLB) and posterior CRLB for IMU/TOA fusion are derived to characterize both the spatial and temporal localization performance of the proposed fusion method. The simulation results show that the proposed fusion method in this paper has obvious spatial-temporal performance advantages in theory. Practical use cases are also conducted, and the experimental results show that the proposed method significantly decreases the drift errors and has a lower tracking error compared with the state of the art. INDEX TERMS Inertial measurement unit (IMU), time of arrival (TOA), Cramér-Rao lower bound (CRLB), posterior Cramér-Rao lower bound (PCRLB), optimization, target tracking.

show abstract

“…In order to reconstruct three-dimensional (3D) propagation environment and to find the main deterministic objects, simultaneous localization and mapping (SLAM) algorithm is used to identify the texture from the measurement scenario picture [140,141]. Figure 4 illustrates our indoor reconstruction result with SLAM algorithm.…”

Section: Identifying the Scatters With The Simultaneous Localization mentioning

confidence: 99%

A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

Xie

Song

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

show abstract

Robust Incremental SLAM Under Constrained Optimization Formulation

Cited by 29 publications

References 32 publications

Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Optimal Estimation and Fundamental Limits for Target Localization Using IMU/TOA Fusion Method

A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

Contact Info

Product

Resources

About