In this paper, we present a novel localization algorithm for mobile robots navigating in complex planar environments, a critical capability for various real-world applications such as autonomous driving, robotic assistance, and industrial automation. Although traditional methods such as particle filters and extended Kalman filters have been widely used, there is still room for assessing the capabilities of modern filtering techniques for this task. Building on a recent localization method that employs a chamfer distance-based observation model, derived from an implicit measurement equation, we explore its potential further by incorporating exact particle flow Daum–Huang filters to achieve superior accuracy. Recent advancements have spotlighted Daum–Huang filters as formidable contenders, outshining both the extended Kalman filters and traditional particle filters in various scenarios. We introduce two new Daum–Huang-based localization algorithms and assess their tracking performance through comprehensive simulations and real-world trials. Our algorithms are benchmarked against various methods, including the widely acclaimed Adaptive Monte–Carlo Localization algorithm. Overall, our algorithm demonstrates superior performance compared to the baseline models in simulations and exhibits competitive performance in the evaluated real-world application.