基于强跟踪的自适应PHD-SLAM算法  

Adaptive PHD-SLAM Algorithm Based on Strong Tracking

作　　者：邓洪高[1,2] 韦凯玲 吴孙勇 邹晗 李明[5] 纪元法 孙少帅 DENG Honggao;WEI Kailing;WU Sunyong;ZOU Han;LI Ming;JI Yuanfa;SUN Shaoshuai(School of Information and Communication,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China;National&Local Joint Engineering Research Center of Satellite Navigation Positioning and Location Service,Guilin,Guangxi 541004,China;Guilin Key Laboratory of Cryptography and Information Security,Guilin,Guangxi 541004,China;School of Mathematics and Computing Science,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China;Guilin Changhai Development Limited Liability Company,Guilin,Guangxi 541004,China)

机构地区：[1]桂林电子科技大学信息与通信学院,广西桂林541004 [2]卫星导航定位与位置服务国家地方联合工程研究中心,广西桂林541004 [3]广西密码学与信息安全重点实验室,广西桂林541004 [4]桂林电子科技大学数学与计算科学学院,广西桂林541004 [5]桂林长海发展有限责任公司,广西桂林541004

出　　处：《信号处理》2024年第11期2074-2084,共11页Journal of Signal Processing

基　　金：国家自然科学基金(62263007,U23A20280,62161007);广西重点研发项目(桂科AB23026147);认知无线电与信息处理教育部重点实验室基金(CRKL210101);桂林电子科技大学研究生教育创新计划项目(2024YCXS116);广西高校数据分析与计算重点实验室开放基金;广西应用数学中心(桂林电子科技大学)开放基金(桂科AD23023002);广西科技厅项目(桂科AA23062038,桂科AD22080061,桂科AB23026120,桂科ZY22096026)。

摘　　要：同时定位与建图(Simultaneous Localization and Mapping,SLAM)技术使移动机器人在缺乏先验环境信息的条件下,能够在估计自身位姿的同时构建环境地图。然而,在海洋、矿洞等复杂环境中,移动机器人容易受到随机突变噪声的干扰,进而导致SLAM性能下降。现有的概率假设密度(Probability Hypothesis Density,PHD)SLAM算法未考虑随机突变噪声,受到干扰时在线自适应调整能力较弱。为解决移动机器人因随机突变噪声导致状态估计和建图精度降低的问题,本文结合强跟踪滤波器(Strong Tracking Filter,STF)与PHD滤波器,提出了一种基于强跟踪的自适应PHD-SLAM滤波算法(Strong Tracking Probability Hypothesis Density Simultaneous Localization and Mapping,STPHD-SLAM)。该算法以PHD-SLAM为框架,针对过程噪声协方差和量测噪声协方差随机突变问题,本文通过在特征预测协方差中引入STF中的渐消因子,实现了对特征预测的自适应修正和卡尔曼增益的动态调整,从而增强了算法的自适应能力。其中渐消因子根据量测新息递归更新,确保噪声突变时每个时刻的量测新息保持正交,从而充分利用量测信息,准确并且快速地跟踪突变噪声。针对渐消因子激增导致的滤波器发散问题,本文对渐消因子进行边界约束,提高算法的鲁棒性。仿真结果表明,在量测噪声协方差和过程噪声协方差随机突变的情况下,所提算法相较于PHD-SLAM 1.0和PHD-SLAM 2.0的定位和建图精度都得到了提高,同时保证了计算效率。Simultaneous localization and mapping technology enables mobile robots to estimate their positions while constructing an environmental map in the absence of prior environmental information.This crucial capability has broad applications,including autonomous navigation,search and rescue operations,and exploration tasks.However,in complex environments,such as oceans,mines,and other challenging terrains,mobile robots are susceptible to interference from random,abrupt noise.This interference,in turn,resulted in a significant decline in SLAM performance as the robots struggled to estimate their positions and map the environment accurately.The probability hypothesis density SLAM algorithm did not adequately account for random abrupt noise,which led to weaker online adaptive adjustment capabilities when disturbed.This limitation hindered the ability of the robot to adapt to sudden changes in the environment,reducing the overall effectiveness of the SLAM process.This study proposed a novel strong tracking probability hypothesis density simultaneous localization and mapping filtering algorithm to address the critical issue of reduced state estimation and mapping accuracy in mobile robots due to random,abrupt noise.This innovative approach integrated the strong tracking filter with the PHD filter,leveraging the strengths of both methods.Built on the PHD-SLAM framework,the proposed algorithm specifically addresses the issue of random,abrupt changes in process noise covariance and measurement noise covariance.These abrupt changes could arise from various sources,including sensor malfunctions,sudden environmental changes,and unexpected dynamic obstacles.The study introduced the fading factor from the strong tracking filter into the feature prediction covariance,achieved adaptive correction of feature predictions,and dynamically adjusted the Kalman gains.This enhancement significantly improved the adaptability of the algorithm to changing conditions,enabling it to maintain accurate localization and mapping performance even in t

关 键 词：同时定位与建图 概率假设密度 强跟踪 渐消因子 

分 类 号：TN713[电子电信—电路与系统]