This paper introduces an innovative approach to Simultaneous Localization and Mapping (SLAM) using the Unscented Kalman Filter (UKF) in a dynamic environment. The UKF is proven to be a robust estimator and demonstrates lower sensitivity to sensor data errors compared to alternative SLAM algorithms. However, conventional algorithms are primarily concerned with stationary landmarks, which might prevent localization in dynamic environments. This paper proposes an Euclidean-based method for handling moving landmarks, calculating and estimating distances between the robot and each moving landmark, and addressing sensor measurement conflicts. The approach is evaluated through simulations in MATLAB and comparing results with the conventional UKF-SLAM algorithm. We also introduce a dataset for filter-based algorithms in dynamic environments, which can be used as a benchmark for evaluating of future algorithms. The outcomes of the proposed algorithm underscore that this simple yet effective approach mitigates the disruptive impact of moving landmarks, as evidenced by a thorough examination involving parameters such as the number of moving and stationary landmarks, waypoints, and computational efficiency. We also evaluated our algorithms in a realistic simulation of a real-world mapping task. This approach allowed us to assess our methods in practical conditions and gain insights for future enhancements. Our algorithm surpassed the performance of all competing methods in the evaluation, showcasing its ability to excel in real-world mapping scenarios.

翻译：本文提出了一种在动态环境中采用无迹卡尔曼滤波（UKF）进行同步定位与地图构建（SLAM）的创新方法。相比其他SLAM算法，UKF被证明是一种鲁棒估计器，且对传感器数据误差的敏感度较低。然而，传统算法主要关注静止路标，这可能导致在动态环境中无法实现定位。本文提出了一种基于欧几里得的方法来处理移动路标，通过计算并估计机器人每个移动路标之间的距离，同时解决传感器测量冲突问题。该方法通过MATLAB仿真实验进行评估，并与传统UKF-SLAM算法进行对比。我们还引入了一个用于动态环境中基于滤波器算法的数据集，可作为评估未来算法的基准。实验结果表明，这种简单而有效的算法减轻了移动路标的干扰影响，这一点已通过移动与静止路标数量、路径点及计算效率等参数的全面验证得到证实。此外，我们还在真实世界地图构建任务的仿真场景中评估了算法性能，从而能够在实际条件下检验方法有效性，并为后续改进提供启示。在评估中，我们的算法超越了所有对比方法，展现出在真实地图构建场景中的卓越性能。