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

Position estimation based on scan matching is a type of popular approaches for mobile robot localization in indoor office-like environments. Global scan matching and local scan matching are two basic position estimation problems. Global scan matching is subject to global localization for mobile robot and local scan matching is used to deal with tracking mobile robot position over time. Global localization based on scan matching is represented in this paper. A global map is built to represent whole environment feature and a local map is built to represent current range scan. Global map and local map consist of a lot of line segments. Complete line segment that represents a complete environment object is singled out and used to match between local map and global map. Line segments are sorted counterclockwise in two maps. This type of ordinal map improves the searching efficiency of matching and localization algorithm is timesaving. In addition, matching between current range scan and global map is based on relative position relationships of line segments in local and global map. Global localization based on relative position relationship avoids frequent coordinates transformation and lowers computational cost. All these techniques have been implemented on our mobile robot ATRVII equipped with 2D laser range scanner SICK.

Occupancy mapping is an important component of a robot navigation system since it enables intelligent planning of actions to accomplish a task. In some cases, occupancy information is mapped as part of a SLAM system. In other cases, such as visual SLAM, an occupancy map must be separately created, harnessing localization information. We present a novel occupancy mapping method that adaptively and efficiently takes advantage of improved trajectory estimates from the localization system, without requiring storage of all poses of the trajectory or the sensor information from all poses. Occupancy information is stored in local maps anchored to poses in the trajectory. Local maps are created adaptively depending on the uncertainty of localization. The local maps maintain consistency of occupancy information independently of changes in the localization map due to loop closures or convergence of the estimation algorithm. At any time, a global occupancy map (snapshot map) can be rendered from the local occupancy maps. We evaluate the performance of the system using real data and compare it to two baseline methods.

This paper presents a laser-based pedestrian tracking system employing a group of mobile robots. Each robot detects pedestrians in its own laser scan images using an occupancy-grid-based method. It then tracks the pedestrians using Kalman filter and global-nearest-neighbor (GNN)-based data association. It also generates a local map using EKF-SLAM. Using inter-robot communication, the robots located near each other exchange information on tracked pedestrians and local maps. Tracking data are fused using the covariance intersection (CI) method, and a global map is built by merging the local maps. In our tracking method, all robots share the information with each other; hence, they can always recognize pedestrians who are invisible to another robot. The method is validated by the results of both indoor and outdoor experiments in which two pedestrians are tracked by three mobile robots. Our tracking system functions in a decentralized manner without any central server.

The recent research trend in deploying mobile robots in the indoor environments require the development of efficient map presentation techniques. A novel method is proposed for building maps of the indoor structured environments by using line-segment relation extracted from laser range scans. Compared with the more common grid representation, the map built with the line segments is more compact and more suitable for the structured environments. The core of the method consists of three parts: local map building, robot pose estimation, and global map update. Compared to two other traditional methods, the proposed method is effective and robust for the indoor environment mapping.

A multi-feature extraction method using a single 2D Laser Range Finder is proposed to provide the information of drivable road region for the Unmanned Ground Vehicle in structured environment. The whole process consists of two main steps: first, the curbs are extracted from the range data using the wavelet transform method, and evaluated by a road scanning model; then, the drivable region is detected based on the average height and flatness. A mathematical model for curb and road detection based on grid map is built to guide the vehicle. The algorithm distinguishes itself from the most existing methods which detect curbs only, it takes advantage of the flatness of surface between extracted curbs is employed as another feature for drivable road region detection. Furthermore, laser intensity data from the same laser range finder is introduced to supply a new restriction for surface evaluation. Experimental results verify the feasibility and stability of the method.

A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps of large environments is proposed. The local map level is composed of a set of local metric feature maps that are guaranteed to be statistically independent. The global level is a topological graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained with local map alignment algorithm, and more accurate estimation is calculated through a global minimization procedure using the loop closure constraint. The local map is built with Rao-Blackwellised particle filter (RBPF). The particle filter is used to extend the path posterior by sampling new poses that integrate the current observation which drastically reduces the uncertainty about the robot pose. The landmark position estimation and update is also implemented through Kalman filter. Omnidirectional vision mounted on the robot tracks the 3D natural point landmarks, which are structured with matching Scale Invariant Feature Transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-Tree. Experimental results on real robot in a medium size, real indoor environment show the practicality and efficiency of our proposed method.

This paper introduces a dual-arm robot painter system which is capable of sketching a camera-captured image with short line segments. To express various curved edges in the image by combining line segments, we first apply edge detection algorithm to the entire image, split the edged image into small boxed pieces, and then apply Hough Transformation to each piece so that the edges inside the piece can be approximated with short line segments. To draw the picture within a reasonable time, we designed a simple dual-arm robot system and controlled both arms concurrently according to linear interpolation algorithm. From the experiments, we could verify that simple linear motions can describe various images effectively with a unique brush style.

In this paper, the use of passive Radio Frequency Identification (RFID) as a support technology for mobile robot navigation and environment mapping is investigated. A novel method for localizing passive RFID tags in a geometric map of the environment using fuzzy logic is, first, described. Then, it is shown how a mobile robot equipped with RF antennas, RF reader, and a laser range finder can use such map for localization and path planning. Experimental results from tests performed in our institute suggest that the proposed approach is accurate in mapping RFID tags and can be effectively used for vehicle navigation in indoor environments.

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

An optimal estimate for electric fields on the shortest line segment between two spherical insulators in three dimensions

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.

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.

This article is concerned with 2D indoor environment mapping produced by a sensor data fusion algorithm. A measurement subsystem assembled on a mobile robot uses its sensors to gather data from its surroundings. In this article, an algorithm is proposed and evaluated to fuse data acquired by the following low-cost sensors: a visual sensor (a wireless webcam with a laser pointer) and three range finder sensors (two infrared units and a sonar transducer). The main steps of the proposed solution are: (a) at each accurate robot pose an occupancy grid (OG) probabilistic map is generated for each type of sensor, (b) the three OG maps are merged using competitive fusion and the RANSAC algorithm is employed to extract line segments, (c) the line segments are further processed using competitive and/or complementary fusion resulting in a feature-based map for each robot pose. The previous steps are repeated until all robots pose are computed. The feature-based maps for all robot poses are then merged using competitive fusion, and a final precise OG map of the environment is generated. The proposed algorithm was evaluated using data gathered by the SLAMVITA robot sensors in two small test environments. The experimental results have shown that the proposed algorithm was able to build precise maps of the test environments (error less than 1.5 %).

This paper presents a comparative study of three algorithms that use occupancy grid map of environments to find a path for a mobile robot from the given start to the goal — the D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> , Two Way D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> (TWD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> ) and E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> . All three algorithms have ability of dynamic replanning in case of changes in the environment. The D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> algorithm produces a path consisted of line segments that have discrete transitions between cell edges — a multiple of 45°. This path is hard to follow by a mobile robot. The TWD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> and E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> algorithms produce more natural paths with continuous headings of the path line segments. The criteria for comparison were the path characteristics, the time of execution and the number of iterations of the algorithms' main while loop. The algorithms were verified both by simulation and experimentally on a Pioneer 3DX mobile robot equipped with a laser range finder.

When pedestrians and a mobile robot pass each other in a corridor, it is important for a robot to make an appropriate path in order not to confuse pedestrians. This paper presents a new path planning method where the mobile robot follows the rule of “eeping to the right.” Three kinds of maps based on laser range finder data are used in the path planning. One is a global cost map made by a simultaneous localization and mapping (SLAM) technique, that expresses a whole environment and is used to make a path from a start position to a goal. Another is a local cost map, that is used for avoiding obstacles near the mobile robot. The other is an attractive force cost map proposed in this paper, that makes the robot keep to the right along a corridor. When there is no pedestrian, the mobile robot goes to a goal position indicated by an operator based on the global cost map while avoiding moving obstacles based on the local cost map in real time. When the robot detects pedestrians, it begins to keep to the right. This method where the robot follows a social norm depending on each country can make a pedestrian pass by the mobile robot more smoothly. Some experimental results reveal the validity of our proposed path planning method.