Adaptive PHD-SLAM Algorithm Based on Strong Tracking

‍ ‍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 the presence of random， abrupt noise. The fading factor was recursively updated based on the measurement innovation. This approach ensured that the measurement innovation at each time step remained orthogonal when noise mutations occurred， thereby fully utilizing measurement information. Consequently， the algorithm could accurately and rapidly track abrupt noise changes， enhancing its robustness and reliability. Additionally， in response to the filter divergence issue caused by the sharp increase in the fading factor， this study imposed boundary constraints on the fading factor. These constraints were designed to prevent excessive values， which could otherwise lead to filter instability and divergence. Simulation results indicated that， in the scenario where random fluctuations occurred in both measurement noise covariance and process noise covariance， the proposed algorithm enhanced localization accuracy and significantly improved mapping precision when compared to the traditional PHD-SLAM 1.0 and the more advanced PHD-SLAM 2.0 algorithms. Additionally， this improvement in accuracy did not come at the expense of computational efficiency； the proposed algorithm maintained high levels of computational efficiency， ensuring it remained practical for real-time applications. These results demonstrated the robustness and effectiveness of the proposed algorithm in handling unpredictable noise variations while delivering superior performance in both localization and mapping tasks.