Incremental Mapping Based on Line-Segments Relation for Mobile Robot

Mobile robot localization and map building are very important for a mobile robot to perform a navigation-based service task to aid the aged or disabled in an indoor environment (office, facilities or at home). In order to enable mobile robot to perform service tasks autonomously, the information of environment in which the mobile robot moves is needed. To obtain the accurate map, many different sensors system and techniques have been developed. Approaches of mapping can roughly be classified according to the kind of sensor data processed and the matching algorithms [S. Thrun et al., 1996, 1998, 2000, 2004; Chong, K.S et al., 1996; C.-C. Wang et al., 2002; G. Dissanayake et al., 2000; B. barshan et al., 1995; Weimin Shen et al., 2005]. In this paper, we proposed a method of map building using interactive GUI for a mobile robot. The reason we proposed this method is that it is difficult for a mobile robot to generate an accurate map although many kinds of sensors are used. The proposed method enables the operator to modify map built by sensors, compared with the real-time video from web camera by using modification tool in interactive GUI. Laser Range Finder (LRF) was used to get the information of the environment in which mobile robot moving. In order to improve self-localization of mobile robot, Extended Kalman Filter (EKF) was introduced. By the results of simulation and experiments, the developed EKF is effective in self-localization of mobile robot. This paper introduces the architecture of the system and gives some experimental results to verify the effectiveness of the developed system. The rest of the paper consists of 5 sections. Section 2 describes the structure of the proposed system. Section 3 presents the developed algorithm of self-localization of mobile robot and gives some simulation and experimental results to verify the effectiveness of the EKF in improving precision of positioning of mobile robot. Section 4 details the developed LRF data processing algorithm. Section 5 introduces the developed interactive GUI. The experimental results are given in Section 6. Section 7 concludes the paper. 13

This paper proposes map building method. Sensors such as a laser range finder, a gyroscope, encoders multiply compose the system. Generally, mobile robot can measure its relative position using a gyroscope and encoders in an environment. However, in this case, a large number of errors occur due to accumulative errors of sensor over time. Therefore, a map based on feature points is used. And an absolute position is measured by the feature points and geometrics. As combining the relative position and the absolute position, mobile robot can recognize its position. According to compositional data of a map, in case of a laser range finder, it takes a long time when adding a map or calculating a position of mobile robot. Therefore, it is necessary to arrange the feature points for computational time and map building. In this paper, a map was formed by laser range finder and features of geometrics. As a result of proposed method, the map was built efficiently in an aspect of time.

Localization and map building are two essential tasks for an autonomous mobile robot’s indoor navigation without a prior map. This paper describes a mobile robot system designed for simultaneous localization and mapping (SLAM) for an autonomous mobile robot in an indoor environment. Due to variant sensor modeling for laser range finder and CCD camera, weighted least square fitting and Canny operator are used to extract certain two-dimensional environmental features and vertical edges respectively. Using Kalman filtering (KF) to localization and grid map building simultaneously are also presented. When implemented on a Zixing mobile robot produced by Harbin Institute of Technology (Weihai), the localization technique correctly localized the robot while exploring and mapping.

Robust simultaneous localization and map building (SLAM) is a key issue for mobile robot in presence of faults. In the paper, an adaptive evolutionary particle filter is designed to achieve robust SLAM for wheeled mobile robot when the laser range finder is subjected to errors. Firstly, a robust perception model for laser range finder is presented. The robustness of the provided model is two folds, (1) error beams of laser range finder are filtered out with segment analysis method, and (2) beams occluded by dynamical objects are filtered out with a high pass filter. Secondly, an adaptive mutation scheme is adopted to recover the diversity of the particles after resampling stage. Lastly, the presented method is testified in a real mobile robot.

Local Map Building for Mobile Robot Autonomous Navigation by Using a 2D Laser Range Sensor

This paper proposes a robust 3D map-building system for a mobile robot using a LRF (Laser Range Finder). The LRF can be utilized and installed on the mobile robot for the map building and the autonomous navigation. The scanning operations are performed while the mobile robot is moving on the ground without stops. The effectiveness of the proposed map building scheme has been demonstrated through the real navigation experiments in the outdoor environment.

Map Building with Line Segments for Sonar Based Mobile Robot Navigations

Exploring unknown environment by multi-robots brings the problem of partial maps integrating. In traditional methods, global map building is almost based on an estimate of robot pose (i.e., its position and orientation). In this paper we propose an improved approach without robots' pose information to integrate maps into a unified coordinate system. Line segment based geometric features in common part of partial maps are used for map matching. Local maps are integrated into a global map based on geometric relationships of line segments. Moreover, we reduce the computation of map matching and improve the accuracy rate of integrating in similar landform. Simulation results show the effectiveness of our approach in structured environment.

Simultaneous localization and mapping (SLAM) is an important system for making fully autonomous robot navigation system. A robot must have a good understanding of the environment for navigation. Mobile robot using fixed laser range finder can only detect obstacle on a particular plane level. This may lead to missing of important obstacles that are both above or below the laser scan level. This will cause the map generated to be inaccurate and collision may happen during autonomous navigation. The Microsoft Kinect sensor can replace a laser range finder in a mobile robot as a low cost alternative while providing 3D data. Using depth image data from Kinect, the obstacles between ground surface and 500mm high are projected to horizontal surface, and converted to laser scan data. This will include the entire obstacles that the robot might collide into. Simulations and experiments have been conducted to test out the method in SLAM process in an indoor environment. Both simulation and experimental results show that the proposed method is capable of getting more detailed map compared to the normal 2D map.

Miniature robots can't carry too many sensors for obstacle avoidance due to its small volume. In this paper, we applied the method based on low-cost local map for obstacle avoidance that uses infrared sensors and MPU6050. Firstly, we introduced the robot and its kinematic model. Secondly, proposed the criterion of robot be trapped. Thirdly, we studied the infrared data filtering with FIR filter and local map building. Fourthly, proposed the free movement area method for two wheeled robot route planning. Finally, we did the contrast experiment, result shows that the robot can escape from the U-shape trap that use the method applied in this paper while the traditional method can't.

Pose estimation of mobile robots is an important issue for many industrial applications. The paper presents an inexpensive solution for pose estimation of mobile robots in indoor environments. Pose estimation is realized by interpreting the received signal strength indicator (RSSI) of RFID tags, which are integrated in the floor and detected by the reader. The paper presents two algorithms for fusing RFID signal strength measurements with odometry based on Kalman filtering. The paper presents experimental results with a Mecanum based omnidirectional mobile robot on a NaviFloor○ ​​​​​​​R installation, which includes passive HF RFID tags. The experiments show that the proposed algorithms provide a better performance compared to the same algorithms which consider the detection of the tags only.

Human-following in a cluttered environment is one of the challenging issues for mobile service robot applications. Since a laser range finder (LRF) is commonly installed for autonomous navigation, it is advantageous to adopt an LRF for detection and tracking humans. In this paper, we aim at the reliable human tracking performances in a dynamic cluttered environment. The key idea is to develop a hierarchical sample-based joint probabilistic data association filter (HSJPDAF) by focusing on the leg positions as well as human positions. The proposed HSJPDAF consists of two levels in order to consider the interdependence between targets. Possible locations of multiple human targets can be simultaneously estimated on the basis of Bayesian filtering. Comparison with the general technique was carried out to verify the performance of HSJPDAF in both artificial indoor and real-world environments. Owing to the hierarchical framework, the proposed method shows the improved robustness by reducing the target loss rate significantly in a dynamic cluttered environment.

The article describes a new auto-charging system designed to enhance successful docking rates for mobile robots while at the same time monitoring the status of the charging current between the mobile robot and the docking station. The communication interface of the mobile robot and the docking station uses a wireless RF interface. The docking station is designed with one active degree of freedom and two passive degrees of freedom. Users tune the location of the charging pins on the docking station to make it possible for the mobile robot to execute an auto-recharging process. The auto-recharging process uses multiple sensors and a laser range finder located on the mobile robot. The laser range finder searches for a landmark to guide the mobile robot towards the docking station. In the experiment, the power of the mobile robot is under its threshold value. The mobile robot transmits a charging command to the docking station via a wireless RF interface. The docking station transmits a location command back to the mobile robot via this wireless RF interface. The mobile robot uses its laser range finder to search for the landmark of the assigned docking station and programs a motion trajectory to move forward to the docking station. The docking station supplies a charging current to the mobile robot by means of a charger. The power detection module simultaneously detects the charging current and the voltage values of the charging process in both the docking station and the mobile robot. The system can monitor the status of the charging process between the docking station and the mobile robot at any time. The power of the mobile robot is enough to be detected by the power detection module. When the charging process is complete, the docking station turns off the charging current and triggers the mobile robot to leave the docking station via the wireless RF interface.

Mobile Robot Self-Localization and Local Map Alignment with a Dempster Shafer Sensor Fusion Algorithm

A new three-dimensional (3D) map building method based on Laser Range Finder (LRF) has been proposed in this research, performing a surface estimation with the Iterative Closest Point (ICP) algorithm. While a mobile robot is navigating in an unknown environment, the entire environment cannot be scanned by LRF since kinematic features of the mobile robot and surface conditions are dynamically changing. To resolve this difficulty in building a 3D map while the mobile robot is navigating, a surface estimation ICP algorithm is proposed, which is based on the continuity of the environment around mobile robot. That is, this new algorithm recovers the un-scanned area by estimating feature points in the neighboring two regions based on the continuous environment information. The effectiveness of proposed algorithm has been demonstrated through real experiments of the mobile robot navigation.