CAJ | 학술논문

"最近邻算法"(NN)是,SLAM领域中广泛使用的一种数据关联算法,它的优点是计算复杂度低,其缺点是关联准确度易受环境影响.为提高"最近邻算法"对环境的鲁棒性,对其进行两处改进:一是从所有观测值之间的相关性出发,消除所有观测值配对结果之间的干涉情况;二是采用多帧观测数据的关联结果动态滤除观测特征中的伪特征.此外,根据机器人的位姿和传感器的有效量程将数据关联限定在局部可能区域中,从而极大地提高了算法的计算效率.仿真和实际实验结果表明,提出的"动态联合最近邻算法"(DJNN)在准确度和计算复杂度方面的性能都很好,具有极强的应用价值.
"최근린산법"(NN)시,SLAM영역중엄범사용적일충수거관련산법,타적우점시계산복잡도저,기결점시관련준학도역수배경영향.위제고"최근린산법"대배경적로봉성,대기진행량처개진:일시종소유관측치지간적상관성출발,소제소유관측치배대결과지간적간섭정황;이시채용다정관측수거적관련결과동태려제관측특정중적위특정.차외,근거궤기인적위자화전감기적유효량정장수거관련한정재국부가능구역중,종이겁대지제고료산법적계산효솔.방진화실제실험결과표명,제출적"동태연합최근린산법"(DJNN)재준학도화계산복잡도방면적성능도흔호,구유겁강적응용개치.
'Nearest Neighbor'(NN) is a widely used data association algorithm in SLAM community.It owns the merit of low computational complexity while maintaining the demerit that its accuracy is susceptible to environments.Two improvements are introduced to enhance the robustness of NN to environments.One is clearing up the interference among all observation mates with a view to the reLativity of all observations.The other is dynamically filtrating spurious features in observed features with the association results of measurements over multiple frames.What's more, data association is limited in potential local region, wbich is determined by pose of the robot and effective range of the sensor.Thus, computational efficiency of NN is improved greatly.Simulative and ex-perimental results indicate that the presented'Dynamic Joint Nearest Neighbor' (DJNN) performs well on both accuracy and com-putational complexity.And it is of wonderful value for real applications.