Abstract
Simultaneous Localization and Mapping (SLAM) technology can make the robot in the unknown area positioning and building the map. Aiming at the problem of the indoor positioning in a small area, SLAM algorithm based on monocular camera was used. Feature based method was introduced to estimate the position of the robot and build the local map, and then BA (Bundle Adjustment) and graph optimization was used to optimize in the back-end. In order to solve the problem of scale drift in positioning, the bag-of-words (BoW) model and loop closing were used to reduce the system error, and an overall map was constructed at last. The experiment results show that the proposed algorithm can construct a sparse map in a small indoor scene, and the positioning error in the x direction reaches 0.0528 m, which meets the accuracy requirements and effectively reduces the scale drift.
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