Quantum Crossover Operator-inspired Salp Swarm Algorithm for relative pose estimation of point clouds

The Salp Swarm Algorithm (SSA) has appeared to be a straightforward yet productive metaphor approach for settling an ample number of optimistic issues. However, the efficacy of standard SSA begins to decline in high-dimensional and dynamic scenarios, where diversity preservation and global optimality prove to be unattainable. A specific instance that presents this challenge is Point Cloud Registration (PCR), which necessitates optimum adjustment of six rigid-body transformation parameters. A refined variant of normal SSA, termed Quantum Crossover Operator SSA (QCO-SSA), is being suggested as an approach frontward to these downsides. This crossover enables the salps to probabilistically recombine the state space solution, producing adaptive offspring salps that sidestep confined minima and preserve distinctive traits. The suggested QCO-SSA framework is applied to the 3D PCR, where it optimally regulates the transformational parameters governing rotational ( R x , R y , R z ) and translational ( T x , T y , T z ). The validation test of QCO-SSA on the Bildstein and Castleblatten Station1 dataset highlights substantial improvements in the positioning accuracy registration adaptability and convergence reliability The suggested QCO-SSA estimates an optimal set of full six-degree transformation parameter of (-3.080,3.091,3.128,-0.308,0.360,-0.975) utilizing Bildstein Station1 and (-3.634,3.666,3.709, -0.363,0.429, -1.134) employing the Castleblatten Station1 dataset. The stated analysis signifies QCO-SSA as a feasible and compelling quantum-swarm hybrid framework for spatial refinements, offering a sustainable and resilient paradigm for the visual analysis and practical 3D perpetual tasks.