Robust View Matching-Based Markov Localization in Outdoor Environments

This paper describes a view-based localization method in outdoor environments. An important issue in view-based localization is to cope with the change of object views due to changes of weather and seasons. We have developed a two-stage SVM-based localization method which exhibits a high localization performance with few parameter tunings. In this paper, we extend the method in the following two ways: (1) adding new object models and visual features to deal with various urban scenes and (2) introducing a Markov localization strategy to utilize the history of movements. The new method can achieve a 100% localization performance in an urban route under a wide variety of conditions. The comparison with local feature-based methods is also discussed.