Abstract
Autonomous system or self-driving car needs to localize itself very frequently or sometimes continuously to determine its proper location that is essential to perform its navigation functionality. The probabilistic models are among the best methods for providing a real-time solution to the localization problem. Current techniques still face some issues connected to the type of representation used for the probability densities. In this paper, we attempt to localize the self-driving car using particle filter with low variance resampling. Particle filter is a recursive Bayes filter, non-parametric approach, which models the distribution by samples [1–3]. A specially modified Monte Carlo localization method is used for extracting the local features as the virtual poles [4, 5]. Simulations results demonstrate the robustness of the approach, including kidnapping of the robot’s field of view [6]. It is faster, more accurate, and less memory-intensive than earlier grid-based methods.
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Iyer, N.C., Kulkarni, A., Shet, R., Keerthan, U. (2021). Localization of Self-driving Car Using Particle Filter. In: Thampi, S.M., Gelenbe, E., Atiquzzaman, M., Chaudhary, V., Li, KC. (eds) Advances in Computing and Network Communications. Lecture Notes in Electrical Engineering, vol 735. Springer, Singapore. https://doi.org/10.1007/978-981-33-6977-1_12
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DOI: https://doi.org/10.1007/978-981-33-6977-1_12
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