Takato Saito

Mobile robot localization using multiple observations based on place recognition and GPS

In this paper, we propose a mobile robot localization system using multiple observations, which show the robots global position. One of observations is GPS observation, the other is utilized an appearance based place recognition. Using GPS observations has still some challenging problems such as multipath and signal lost under the environments there is tall buildings nearby. It affects a significant error on localization. On the other hand, appearance based place recognition methods are efficient to recognize the robots global position. It becomes possible to use a scene database with global position information. However, it could fail to function properly in natural environments like a lawn grass or trees in a park. We solve these demerits of each observations by using these multiple observations. Our system uses not only multiple observations but also dead reckoning with Gyrodometry model. As a result, the proposed localization system have achieved robust localization. To verify the validity of proposed method, our experiments using 1600m outdoor course in different seasons were conducted.

Mobile robot localization by GPS and sequential appearance-based place recognition

In this paper, we propose a mobile robot localization by GPS observations and appearance-based place recognition. These are critical issues for achieving a high accuracy and stable localization due to some challenging problems that GPS observations face still such as multipath and signal lost, especially under situations where there are narrow streets and so on. The appearance-based place recognition method that is combined with positional information has capability to overcome the issue. We apply both of observations derived from GPS and appearance-based place recognition to a mobile robot localization for the sake of achieving robust localization. Moreover sequential appearance-based place recognition makes it possible to recognize their own position even when we navigate a robot on a night. Our system uses not only multiple observations but also dead reckoning with gyrodometry model. To verify the validity of the proposed method, our experiment is conducted through an outdoor course.

Localization independent of location based on place recognition and GPS observations

In this research, we propose a mobile robot localization system using multiple observations, which show the robots global position. One of observations is GPS observations, another is utilized an appearance based place recognition. Using GPS observations faces still some challenging problems such as multipath and signal lost under the environments there is tall buildings nearby. These are critical issues for achieving a high accuracy and stable localization. On the other hand, appearance based place recognition methods are efficient to recognize the robots global position. It becomes possible to use a scene database with global position information. However appearance based place recognition methods could fail to function properly in natural environments like a lawn grass or trees in a park. We solve these disadvantages of each observations by using these multiple observations. Our system uses not only multiple observations but also dead reckoning with Gyrodometry model. Therefore, proposed method localize a robot position robustly indoors or not. To verify the validity of proposed method, our experiments are conducted about 1600m outdoor course in different seasons and course through an indoor.

Mobile robot localization system in frequent GPS-denied situations

In this paper, we propose a mobile robot localization system in frequent GPS-denied situations. We utilize multiple observations that are obtained from sequential appearance-based place recognition and GPS. Using GPS observations has still some challenging problems such as multipath or signal lost under environments where there are tall buildings nearby. The appearance-based place recognition that is combined with positional information has capability to overcome the issue. Nevertheless, GPS observations which are obtained in the situation sometimes have better quality (e.g. Precision or accuracy) than positional information from the place recognition because those coordinates always have some errors. We apply both of observations to a mobile robot localization for the sake of achieving robust localization. Moreover sequential appearance-based place recognition makes it possible to recognize their own position even when we navigate a robot at night. Our system uses not only multiple observations but also dead reckoning with the gyrodometry model. Our experiments are performed over aggregate 5300 m trajectory approximately that contains three times trials through a 1600 m outdoor route in different seasons and at different times, and once trail through a 500 m short-range route to verify its validity.