Mobile Robot Indoor Localization Research Articles

Integrating WSN and Laser SLAM for Mobile Robot Indoor Localization

Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for the following path planning task. In an indoor environment where the global positioning system signal fails or becomes weak, the wireless sensor network (WSN) or simultaneous localization and mapping (SLAM) scheme gradually becomes a research hot spot. WSN method uses received signal strength indicator (RSSI) values to determine the position of the target signal node, however, the orientation of the target node is not clear. Besides, the distance error is large when the indoor signal receives interference. The laser SLAM-based method usually uses a 2D laser Lidar to build an occupancy grid map, then locates the robot according to the known grid map. Unfortunately, this scheme only works effectively in those areas with salient geometrical features. The traditional particle filter always fails for areas with similar structures, such as a long corridor. To solve their shortcomings, this paper proposes a novel coarse-to-fine paradigm that uses WSN to assist mobile robot localization in a geometrically similar environment. Firstly, the fingerprints database is built in the offline stage to get reference distance information. The distance data is determined by the statistical mean value of multiple RSSI values. Secondly, a hybrid map with grid cells and RSSI values is constructed when the mobile robot moves from a starting point to the ending place. Thirdly, the RSSI values are thought of as a basic reference to get a coarse localization. Finally, an improved particle filtering method is presented to achieve fine localization. Experimental results demonstrate that our approach is effective and robust for global localization. The localization success rate reaches 97.0% and the average moving distance is only 0.74 meters, while the traditional method always fails. In addition, the method also works well when the mobile robot is kidnapped to another position in the environment.

Mobile Robot Indoor Localization Using Color-Coded Beacons and a Depth Camera

This paper aims to design and implement an indoor localization method for mobile robots based on trilateration technique, Color Code signatures (CCs) as artificial passive beacons, and a depth camera. The artificial passive beacons are designed as three disks positioned one on top of the other’s with different sizes and colors (primary colors only, RGB). The three disks share the same center. The designed beacons are placed on the ceiling to be visible from most locations within the room. The robot's model, controller, and localization method are implemented and evaluated inside the CoppeliaSim environment. The simulation results show that the CCs detecting algorithm enables the robot to discover the designed beacons, achieve an accurate localization and reach the target

An EKF-based multiple data fusion for mobile robot indoor localization

Purpose Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments. Design/methodology/approach Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments. Findings The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation. Originality/value Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.

Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion With Application to Mobile Robot Localization

In this paper, we propose a new state estimator called the two-layer nonlinear finite impulse response (TLNF) filter and adopt this new filter and unscented Kalman filter (UKF) as subfilters to create the fusion TLNF/UK filter. The TLNF filter is constructed with measurements that are redefined by weighting the estimated states acquired through minimizing the cost function based on the Frobenius norm. The efficient iterative form of the TLNF filter is also developed in this paper. Using the fact that the UKF and the TLNF filter each takes a different type of memory structure, the fusion TLNF/UK filter is designed as a robust nonlinear state estimator taking both advantages of each filter. To obtain the best fusion estimates, probabilistic weights are computed based on Bayes’ rule and the likelihood of each filter. Both simulation and experimental results for mobile robot indoor localization have shown that the fusion TLNF/UK filter achieves a higher level of accuracy and robustness under practical situations.

OS17-2 Optimization of Wireless Nodes Placement for Mobile Robot Indoor Localization(Robotics and Mechatronics (1),OS17 Robotics and mechatronics,APPLICATIONS)

OS17-2 Optimization of Wireless Nodes Placement for Mobile Robot Indoor Localization(Robotics and Mechatronics (1),OS17 Robotics and mechatronics,APPLICATIONS)

선택적 Anchors 기반 Indoor GPS 및 EKF를 이용한 이동 로봇 위치 추정

This paper proposes a technique of indoor localization for mobile robot by so called indoor GPS and EKF. Basically the concept of indoor GPS is similar outdoor GPS, and the indoor GPS gets distances between Anchors and Tag by a ranging method of CSS and then estimates the coordinate by distances and known Anchor positions. After we performed performance test of indoor GPS system in ideal and multipath environment, we configured that the indoor GPS has internal error factors and external error factors. This paper handled a multipath problem belonging to external error factors. At first various direct physical method are introduced to fix the multipath problems, and as expected we got errors corrected considerably. And then the method of selective anchors for indoor GPS is applied. With these two level improvement of indoor GPS performance, EKF(Extended Kalman Filter) is applied to mobile robot in indoor environment. The usefulness of the proposed methods are shown by a series of experiments in a environment giving contaminated data by multipath.

1A2-E12 天井目地模様などを用いた移動ロボットの屋内自己位置推定

1A2-E12 天井目地模様などを用いた移動ロボットの屋内自己位置推定