  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.
