Localization Fusion Framework Based on Track-to-Track Fusion With Bias Correction

Abstract

The importance of precise localization technology for the autonomous driving of industrial mobile robots is steadily increasing. Notably, research into enhancing accuracy and robustness by fusing multiple systems is actively conducted rather than relying on a single localization system. We highlight the use of track-to-track (T2T) fusion, which takes the localization results of independent systems as input. This approach eliminates system adjustments with sensor changes, offering benefits for industrial mobile robots. However, existing T2T-based fusion methods suffer from overlooking slowly changing biases that can gradually increase over time due to sensor drift errors, map biases, etc. Since biases have different values and frequencies for each system, they are challenging for conventional T2T methods to handle. This article proposes a localization fusion framework that tackles such slowly varying biases. First, estimating the distinct biases inherent to each system poses a challenging problem; therefore, we align them to a single common bias. Second, localization estimates with a common bias are fused using a split covariance intersection filter, one of the T2T fusion techniques, considering the independence and correlation within each system to ensure fusion consistency. The proposed method has been validated in both simulation and real-world environments, confirming superior performance compared to existing algorithms.