Motion estimation of indoor robot based on image sequences and improved particle filter

Robot motion estimation is fundamental in most robot applications such as robot navigation, which is an indispensable part of future internet of things. Indoor robot motion estimation is difficult to be resolved because GPS (Global Positioning System) is unavailable. Vision sensors can provide larger amount of image sequences information compared with other traditional sensors, but it is subject to the changes of light. In order to improve the robustness of indoor robot motion estimation, an enhanced particle filter framework is constructed: firstly, motion estimation was implemented based on the distinguished indoor feature points; secondly, particle filter method was utilized and the least square curve fitting was inserted into the particle resampling process to solve the problem of particle depletion. The various experiments based on real robots show that the proposed method can reduce the estimation errors greatly and provide an effective resolution for the indoor robot localization and motion estimation.