A survey on nonlinear applications of modified particle filter

Vishak, P; Sudheesh, P.; Jayakumar, M

doi:10.1109/wispnet.2017.8299924

Cited by 2 publications

(1 citation statement)

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“…This flexibility to handle non-linear and non-Gaussian systems is the principal advantage of Particle Filters. They have been applied in complex environments where the dynamics are not easily modeled by linear equations (Vishak et al, 2017). The computational intensity of Particle Filters makes them less suited for real-time applications, especially when the number of particles required for accurate state estimation is high.…”

Section: Particle Filtersmentioning

confidence: 99%

Intelligent Systems in Motion

Cai,

Qin,

et al. 2023

International Journal on Semantic Web and Information Systems

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Simultaneous localization and mapping (SLAM) serves as a cornerstone in autonomous systems and has seen exponential growth in its roles, particularly in facilitating advanced path planning solutions. One emerging avenue of research that is rapidly evolving is the incorporation of multi-sensor fusion techniques to enhance SLAM-based path planning. The paper initiates with a thorough review of various sensor types and their attributes before covering a broad spectrum of both traditional and contemporary algorithms for multi-sensor fusion within SLAM. Performance evaluation metrics pertinent to SLAM and sensor fusion are explored. A special focus is laid on the interconnected roles and applications of multi-sensor fusion in SLAM-based path planning, discussing its significance in navigation scenarios as well as addressing challenges such as computational burden and real-time implementation. This paper sets the stage for future developments in creating more robust, resilient, and efficient SLAM-based path planning systems enabled by multi-sensor fusion.

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Section: Particle Filtersmentioning

confidence: 99%

Intelligent Systems in Motion

Cai,

Qin,

et al. 2023

International Journal on Semantic Web and Information Systems

View full text Add to dashboard Cite

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Multi-Modal Pose Representations for 6-DOF Object Tracking

Majcher,

Kwolek

2024

J Intell Robot Syst

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Pose estimation methods for robotics should return a distribution of poses rather than just a single pose estimate. Motivated by this, in this work we investigate multi-modal pose representations for reliable 6-DoF object tracking. A neural network architecture for simultaneous object segmentation and estimation of fiducial points of the object on RGB images is proposed. Given a priori probability distribution of object poses a particle filter is employed to estimate the posterior probability distribution of object poses. An advanced observation model relying on matching the projected 3D model with the segmented object and a distance transform-based object representation is used to weight samples representing the probability distribution. Afterwards, the object pose determined by the PnP algorithm is included in the probability distribution via replacing a particle with the smallest weight. Next, a k-means++ algorithm is executed to determine modes in a multi-modal probability distribution. A multi-swarm particle swarm optimization is then executed to determine the finest modes in the probability distribution. A subset of particles for final pose optimization is found in a multi-criteria analysis using the TOPSIS algorithm. They are verified using conflicting criteria that are determined on the basis of object keypoints, segmented object, and the distance transform. On the challenging YCB-Video dataset it outperforms recent algorithms for both object pose estimation and object pose tracking.

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A survey on nonlinear applications of modified particle filter

Cited by 2 publications

References 40 publications

Intelligent Systems in Motion

Intelligent Systems in Motion

Multi-Modal Pose Representations for 6-DOF Object Tracking

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