## 1 Introduction
Each cell holds the probability of being free or occupied. On the other hand, Landmark-Based Maps consist of a set of points, commonly called _landmarks_, whose position is fixed in the space. The robot's pose estimation is based on the measurement of its distance from the observed landmarks. In this case, each landmark \(l\) is identified with a 3D vector \([x_{l},y_{l},l]\) containing its coordinates with respect to the map origin \([x_{l},y_{l}]\) and its numerical identifier \(l\in[0,N_{l})\). While Occupancy Grids have higher information content, Landmark-Based Maps provide a more compact environmental representation, reducing memory consumption [23; 24]. Optimization-based methods model the map as factor graphs. They can encode information at a geometric level (e.g., points, lines, planes) and a semantic level (instances of objects stored in a previously defined database). Further insights on the main state-of-the-art SLAM proposals available are given in review works such as [12; 13; 25; 26; 27; 28; 29]. While filter-based SLAM is more employed for _online SLAM_, where the latest robot pose is estimated jointly with the map, optimization-based SLAM is mainly leveraged to solve the _full SLAM_ problem, where the whole trajectory is estimated [12]. This work focuses on particle filter-based algorithms, specifically FastSLAM 2.0, based on Landmark-Based Maps. FastSLAM 2.0 leverages a Rao-Blackwellized particle filter where the robot's pose and the map estimations are decoupled. In more detail, the robot pose estimation is first predicted according to the motion model and the control inputs, then adjusted considering the latest associated observations. On the other hand, each landmark position is estimated using an extended Kalman Filter. Further details on FastSLAM 2.0 are discussed in Section 2. One of the main limitations of SLAM approaches is their high computational complexity, and particle filter-based methods are no exception. In addition, SLAM should often run on onboard devices and guarantee real-time pose estimation. In this context, parallel programming and devices enabling massive parallelization, such as Graphic Processing Units (GPUs), can enable many SLAM methodologies to meet real-time constraints [30; 31; 32; 33; 34]. PF-based approaches can particularly benefit from acceleration via parallel programming due to their modular structure. In fact, particles are calculated independently and, within each particle, the landmark poses are also independently estimated. In this context, the most commonly adopted acceleration modality in the literature is particle-wise parallelization, as in [35; 36; 37; 38; 39]. Some proposals focus the acceleration efforts on specific steps of the pipeline, such as data association [40; 41], resampling [42; 43; 44], and particle weight computation [37]. As for FastSLAM 2.0, a few works provide parallelized implementations on the edge. An accelerated implementation for FPGA is available in [45], while a parallelized implementation of the algorithm for the OMAP4430 architecture is available at [46]. To the best of our knowledge, the only parallelized implementation of FastSLAM 2.0 on a GPU device is discussed in [39], where a particle-wise parallelization for monocular inertial SLAM is proposed. The FAST Corner Detection method carries out the data association step on the CPU. The parallelization process requires identifying the parameters that affect the algorithm's computational complexity to distribute the computation among the single multiprocessors efficiently. In some cases, such as in FastSLAM 2.0, the complexity can be determined by multiple parameters, which may depend on the use case scenario of interest (i.e., _the context_), providing multiple possibilities in the parallelization design. In addition, the specific characteristics of the hardware architecture in use significantly impact the performance in terms of latency.