  This work introduces SynPF, an MCL-based algorithm tailored for high-speed
racing environments. Benchmarked against Cartographer, a state-of-the-art
pose-graph SLAM algorithm, SynPF leverages synergies from previous
particle-filtering methods and synthesizes them for the high-performance racing
domain. Our extensive in-field evaluations reveal that while Cartographer
excels under nominal conditions, it struggles when subjected to wheel-slip, a
common phenomenon in a racing scenario due to varying grip levels and
aggressive driving behaviour. Conversely, SynPF demonstrates robustness in
these challenging conditions and a low-latency computation time of 1.25 ms on
on-board computers without a GPU. Using the F1TENTH platform, a 1:10 scaled
autonomous racing vehicle, this work not only highlights the vulnerabilities of
existing algorithms in high-speed scenarios, tested up until 7.6 m/s, but also
emphasizes the potential of SynPF as a viable alternative, especially in
deteriorating odometry conditions.
