L1-Lp Minimization via a Distributed Smoothing Neurodynamic Approach for Robust Multi-View Three-Dimensional Space Localization
Source abstract
Abstract
This paper presents a distributed smoothing neurodynamic approach for solving the L1-Lp minimization problem, with application to robust and collaborative multi-view three-dimensional (3D) space localization. To handle the non-Lipschitz continuity gradients, a smooth approximation technique is introduced, yielding a distributed neurodynamic model that integrates classical smoothing neural networks with multi-agents consensus theory. Theoretical analysis guarantees the global convergence of each agent’s state to the optimal solution. The stability and convergence of the proposed approaches are rigorously proved using Lyapunov theory. Numerical experiments on multi-view 3D space localization in the presence of measurement noise demonstrate the method’s effectiveness and practical value for distributed visual computing.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotics and Sensor-Based Localization · Advanced Vision and Imaging · Distributed Control Multi-Agent Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronics and Information Engineering, Southwest University, Chongqing 400715, China
Chongqing General Station of Exit and Entry Frontier Inspection, Chongqing 401147, China
School of Computer Science, Chongqing University, Chongqing 400044, China
Funding
Natural Science Foundation of China
References (49)
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