Point-Based Scan Matching by Differential Evolution

P. Krömer, Jaromír Konecny, Michal Prauzek
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引用次数: 4

Abstract

Nature -- inspired metaheuristics have been applied in many different areas and have shown good results in comparisonwith traditional domain -- specific optimization methods. In thiswork, we investigate the ability of a simple variant of differentialevolution to solve 2D scan matching problem. It consists in findingan optimum affine transformation (rotation and translation) between two laser scans (2D pointclouds). Parameters of the affine transformation are in this approach determined by differential evolution. All steps of the proposed algorithm are data parallel and can be efficiently accelerated by massively parallel platforms including mobile graphical processing units. The proposed method was implemented and experimentally evaluated on a test data set. The obtained results show that it achieves a good accuracy and is a promising technique for real -- world applications in mobile robotics.
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基于点的差分进化扫描匹配
自然启发的元启发式已经应用于许多不同的领域,并与传统的特定领域优化方法相比显示出良好的效果。在这项工作中,我们研究了微分进化的一个简单变体来解决二维扫描匹配问题的能力。它包括在两个激光扫描(2D点云)之间找到最佳仿射变换(旋转和平移)。仿射变换的参数在这种方法中由微分演化决定。该算法的所有步骤都是数据并行的,可以通过包括移动图形处理单元在内的大规模并行平台有效地加速。在一个测试数据集上对该方法进行了实验验证。结果表明,该方法具有良好的精度,在实际应用中具有广阔的应用前景。
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