Misalignment Recognition Using Markov Random Fields With Fully Connected Latent Variables for Detecting Localization Failures

Recognizing misalignment between sensor measurements and objects that exist on a map due to inaccuracies in localization estimation is challenging. This can be attributed to the fact that the sensor measurements are individually modeled for solving the localization problem, resulting in entire relations of the measurements being ignored. This letter proposes a misalignment recognition method using Markov random fields with fully connected latent variables for the detection of localization failures. The proposed method estimates the classes of each sensor measurement that are aligned, misaligned, and obtained from unknown obstacles. The full connection allows us to consider the entire relation of the measurements. A misalignment can be exactly recognized even when partial sensor measurements overlap with mapped objects. Based on the class estimation results, we are able to distinguish whether the localization has failed or not. The proposed method was compared with six alternative methods, including a convolutional neural network, using datasets composed of success and failure localization samples. Experimental results show that the classification accuracy of the localization samples using the proposed method exceeds 95% and outperforms the other examined methods.