Robust capsule-robot positioning with limited magnetic observations: An inertial-enhanced approach

The capsule robot has become an important tool in covering the entire spectrum of digestive tract disease diagnosis. To achieve magnetic capsule-robot localization, the Levenberg-Marquardt (LM) algorithm has become a mainstream approach that provides accurate solutions in the general case. In practice, however, to meet the requirements of wearability, fewer sensors and lower power consumption are required. When the number of sensor observations becomes smaller, local convergences and outliers may occur in positioning results. To mitigate this issue, this paper makes two contributions to enhance the robustness of capsule-endoscope positioning, especially when the quality of magnetic observations is low. First, it proposes a two-step approach that initializes the capsule attitude by using inertial measurements before estimating the position. Second, it presents an improved LM-based positioning algorithm based on vest-type magnetic sensor arrays. Furthermore, to verify the proposed approach, a vest-type wearable device with two low-cost magnetometer arrays is designed. Test results have shown the effectiveness of the proposed LM method in enhancing positioning when there is a lack of observations.