Autonomous Navigation Based on Magnetic and Geometric Landmarks on Environmental Structure in Real World

Akai, Naoki; Inoue, Kazumichi; Ozaki, Koichi

doi:10.20965/jrm.2014.p0158

Cited by 21 publications

(2 citation statements)

References 10 publications

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“…The trajectories by each method are shown in Figure 7. The trajectories were obtained by using our localization method [20], and obviously outlier was manually corrected. As can be seen from Figure 7, only the proposed method and the MNPC could complete the autonomous navigation.…”

Section: Navigation Experimentsmentioning

confidence: 99%

Development of magnetic navigation method based on distributed control system using magnetic and geometric landmarks

et al. 2014

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Background:In order for a robot to autonomously run in outdoor environments, a robust and stable navigation method is necessary. Especially, to run in real-world environments, robustness against moving objects is important since many pedestrians and bicycles come and go. Magnetic field, which is not influenced by the moving objects, is considered to be an effective information for autonomous navigation. Methods: Localization technique using a magnetic map, which records ambient magnetic field, has been proposed. The magnetic map is expressed as a linear map. When using this linear magnetic map, swerving from the desired path is a fatal problem. It is because that the magnetic map contains only magnetic data on a desired path. In the paper, we propose a novel navigation method which allows a robot to precisely navigate on a desired path even if localization is performed on the basis of the linear magnetic map. The navigation is performed by using a control method based on a DCS (Distributed Control System). In the system, several navigation modules are executed in parallel, and they independently control the robot by using magnetic and geometric landmarks. Results and discussion: We conducted three navigation experiments. Our robot could perfectly accomplish all navigation even if it was disturbed by many moving objects during the navigation. Conclusions:The control method based on the DCS could switch the navigation module for controlling the robot to cope against the change of its surroundings. The precise and robust navigation was achieved with the proposed method.

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Section: Navigation Experimentsmentioning

confidence: 99%

Development of magnetic navigation method based on distributed control system using magnetic and geometric landmarks

et al. 2014

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“…A robot which collect environmental data is a same robot which performing navigation typically [1][2] [3]. On the other hand, we adopt a bicycle.…”

Section: Introductionmentioning

confidence: 99%

Environmental mapping by a riding bicycle for a navigation map of autonomous mobile robot

Okamoto

Takei

2015

2015 IEEE/SICE International Symposium on System Integration (SII)

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Recently autonomous navigation technique for a mobile robot becomes very important. In a field of navigation, the technique for localization and mapping by laser range finder is one of the major interesting topic. In order to use the method, an environment map including navigation root is need to be prepared in advance. Then, it is necessary to travel the route once before to make the map and the route by a mobile robot which performs the autonomous navigation. In many cases, when collecting environmental data, the robot requires a person to be accompanied. When making a wide range of environmental map, the person needs to walk long distance, and the load of the person increases. Therefore, we suggest to use bicycle installed LIDER and ride it to get the information of environment of the map. By using the bicycle, it is considered that the cost and time to collect the information of environment becomes low and short. In this work, we report an experiment of mapping in riding a bicycle equipped with a laser range finder, gyro sensor and encoder, and an experiment of a navigation by a mobile robot with the map created by the bicycle.

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Hybrid Localization using Model- and Learning-Based Methods: Fusion of Monte Carlo and E2E Localizations via Importance Sampling

Akai

Hirayama

Murase

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents an efficient solution to 3D-LiDAR-based Monte Carlo localization (MCL). MCL robustly works if particles are exactly sampled around the ground truth. An inertial navigation system (INS) can be used for accurate sampling, but many particles are still needed to be used for solving the 3D localization problem even if INS is available. In particular, huge number of particles are necessary if INS is not available and it makes infeasible to perform 3D MCL in terms of the computational cost. Scan matching (SM), that is optimization-based localization, efficiently works even though INS is not available because SM can ignore movement constraints of a robot and/or device in its optimization process. However, SM sometimes determines an infeasible estimate against movement. We consider that MCL and SM have complemental advantages and disadvantages and propose a fusion method of MCL and SM. Because SM is considered as optimization of a measurement model in terms of the probabilistic modeling, we perform measurement model optimization as SM. The optimization result is then used to approximate the measurement model distribution and the approximated distribution is used to sample particles. The sampled particles are fused with MCL via importance sampling. As a result, the advantages of MCL and SM can be simultaneously utilized while mitigating their disadvantages. Experiments are conducted on the KITTI dataset and other two open datasets. Results show that the presented method can be run on a single CPU thread and accurately perform localization even if INS is not available.

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Autonomous Navigation Based on Magnetic and Geometric Landmarks on Environmental Structure in Real World

Cited by 21 publications

References 10 publications

Development of magnetic navigation method based on distributed control system using magnetic and geometric landmarks

Development of magnetic navigation method based on distributed control system using magnetic and geometric landmarks

Environmental mapping by a riding bicycle for a navigation map of autonomous mobile robot

Hybrid Localization using Model- and Learning-Based Methods: Fusion of Monte Carlo and E2E Localizations via Importance Sampling

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