Masahito Mitsuhashi

Mobile robot localization using appearance based place recognition

In this paper, we propose a mobile robot localization system using appearance based place recognition. Our system uses appearance based place recognition and dead reckoning with Gyrodometry model. We use the theory of the observation likelihood estimation introduced from Fast Appearance Based Mapping (FAB-MAP) for appearance based place recognition. Images collected by a robot produce Bag-of-Words representation and Chow Liu trees. This representation is based on quantized SURF detectors/descriptors. A certain place can be recognized when the image collected matches a reference image coming from the same place in scene database. In addition, problems of appearance based place recognition techniques are solved by using sensor fusion. Experimental results are presented in an outdoor environment. The proposed localization system can achieve the average robots position error within 2% without GPS in an outdoor environment.

Correcting angle of visual odometry system by fusing monocular and stereo methods in untextured dynamic environment

In this research, we propose state-of-the-art 6-Degrees-Of-Freedom (6DOF) visual odometry (VO) system which fused stereo VO with effective feature sampling, VO using normal vector information of a ground plane and monocular VO. Firstly, stereo VO is used to do 6DOF motion estimation in untextured dynamic environments. Secondly, ground plane information is used to improve the accuracy of roll and pitch angles estimation. Thirdly, monocular VO is introduced to solve the problems in stereo processing. Finally, Unscented Kalman Filter is adopted for the fusion of the information acquired from three kinds of VO techniques to accomplish robust and accurate localization. We present and evaluate experimental results for our system over 730 meters runs in a challenging outdoor environment and compare it with ground truth. Proposed VO system can localize the robots position within 3.2% error in untextured outdoor environment.

Noise model creation for visual odometry with neural-fuzzy model

In this paper, we propose a technique of learning a noise pattern of visual odometry for accurate and consistent 6DOF localization. The noise model is represented by three parameters of feature points as input: I) The number of inliers among feature points, II) Average of distances between feature points, III) Variance of interior angles. The correlation between these parameters and estimation error is also described. To approximate the complicate noise model accurately, our technique adopts Hybrid neural Fuzzy Inference System (HyFIS) for a learning engine. The noise model is created with HyFIS beforehand, and then the error of visual odometry is estimated by the noise model and compensated on the fly. Learning results of the noise model and results of 6DOF localization in untextured and dynamic environments are presented, effectiveness of our technique is shown.

Autonomous mobile system in urban environments with moving obstacles such as people and another robots

In this paper, we propose an autonomous navigation system in urban environments. In order to create a useful navigation system in urban environments, the system examines whether the area near around is traversable or not in real time, instead of using prior knowledge of dense maps. To examine the traversability robustly, the system adopts the following technologies. The system involves a localization with the information of GPS and dead reckoning, and integration method using Divided Difference Filter (DDF). DDF is an appropriate localization method using truncated data of GPS in urban areas. The system also estimates traversability of road regions using a laser range finder (LRF). By estimating traversability, robot is able to run safely on the route even if localization had poor accuracy. Lastly, the system detects moving obstacles using LRF, because the robot must be operated in environments where many people would exist. The effectiveness of the proposed system is proved through some experiments in outdoor environments including various situations.

Mobile robot localization using place recognition in outdoor environments with similar scenes

In this paper, we propose a mobile robot localization system using appearance based place recognition. Our system uses appearance based place recognition and dead reckoning with Gyrodometry model. We use theory of the observation likelihood estimation introduced from Fast Appearance Based Mapping (FAB-MAP) for appearance based place recognition. Images collected by a robot produce Bag-of-Words representation and Chow Liu trees. This representation is based on quantized SURF detectors/descriptors. A certain place can be recognized when the image collected matches a reference image coming from the same place in the scene database. In addition, problems of appearance based place recognition techniques are solved by using sensor fusion. Experimental results are presented in an outdoor environment. The proposed localization system can achieve the average robots position error within 2% without GPS in an outdoor environment.

Autonomous mobile system unaffected by moving obstacles in urban environments

In this paper, we propose an autonomous navigation system in urban environments. In order to create a useful navigation system in urban environments, the system examines whether the area near around is traversable or not in real time, instead of using prior knowledge of dense maps. To examine the traversability robustly, the system adopts the following technologies. The system involves a localization with the information of GPS and dead reckoning, and integration method using Divided Difference Filter (DDF). DDF is an appropriate localization method using truncated data of GPS in urban areas. The system also estimates traversability of road regions using a laser range finder (LRF). By estimating traversability, robot is able to run safely on the route even if localization had poor accuracy. Lastly, the system detects moving obstacles using LRF, because the robot must be operated in environments where many people would exist. The effectiveness of the proposed system is proved through some experiments in outdoor environments including various situations.

Integrated Visual Odometry System with Three Kind of Stereo and Monocular Methods in Untextured Dynamic Environment

Abstract In this research, we propose state-of-the-art 6-Degrees-Of-Freedom (6DOF) visual odometry (VO) system which fused stereo VO with effective feature sampling, VO using normal vector information of a ground plane and monocular VO. Firstly, stereo VO is used to do 6DOF motion estimation in untextured dynamic environments. Secondly, ground plane information is used to improve the accuracy of roll and pitch angles estimation. Thirdly, monocular VO is introduced to solve the problems in stereo processing. Finally, Unscented Kalman Filter is adopted for the fusion of the information acquired from three kinds of VO techniques to accomplish robust and accurate localization. We present and evaluate experimental results for our system over 730 meters runs in a challenging outdoor environment and compare it with ground truth. Proposed VO system can localize the robots position within 3.2% error in untextured outdoor environment.