Freedom of mobility is a crucial aspect of our daily lives. Consequently, engineering solutions for mobility, including smart wheelchairs, are becoming increasingly important for those with disabilities. However, the lack of a reliable solution for indoor localization has affected the pace of research in this direction. GPS signals cannot be measured indoors and environment modifications for wheelchair localization can be expensive and intrusive. This research explores the feasibility of using ambient magnetic fields for indoor localization by exploiting the spatial non-uniformity due to ferromagnetic objects in ordinary working environments. A non-parametric density estimation technique was developed to build magnetic field maps. This approach is compared to an existing regression technique. Two different approximate kinematic models for the wheelchair are presented and implemented in a particle-filtering framework. Finally, the efficacy of these mapping techniques and motion models, including and excluding odometry information, are compared via tracking experiments conducted with a smart wheelchair.

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