Extrinsic Calibration of Multiple Two-Dimensional Laser Rangefinders Based on a Trihedron

The combination of a camera and a 2D laser rangefinder (LRF) is widely used in robotics, mapping, and unmanned driving to simultaneously obtain the 3D geometry and color texture of a scene. However, data misregistration between the camera and the LRF frequently occurs due to the difficulty of precise installation and alignment between them. Extrinsic calibration between the camera and the LRF is necessary. In this study, a photogrammetric control field is used to perform a robust and accurate calibration between the camera and the LRF which requires only one shot. With the use of the photogrammetric control field as the common reference, extrinsic calibration between two sensors is decoupled by calibrating each sensor separately. First, using the room corner of the control field, the LRF is calibrated with respect to the control field by solving a simplified perspective-three-point problem. Then, a large number of control points are used to obtain the robust and accurate extrinsic parameters of the camera with respect to the control field. Experiments with simulated and real data were performed. The experimental results show that the proposed scheme is accurate, precise, and robust under different noise levels, and the calibration results can be utilized in indoor and outdoor applications.

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.

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.

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 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.

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.

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.

The objective of this study is to develop a robust human tracking method for mobile robot using only one laser range finder (LRF). When a mobile robot runs along a human, it is always necessary to observe positions of the human. Therefore, the LRF must be mounted on a position easy to recognize human. Furthermore, the LRF may lose the position of the target human, and another human might enter between the robot and the target human. Therefore, this paper discusses the mounting position of the LRF, as well as a robust tracking method using the LRF.

This paper presents a novel extrinsic calibration algorithm between a binocular stereo vision system and a 2D LIDAR (laser range finder). Extrinsic calibration of these heterogeneous sensors is required to fuse information obtained separately by vision sensor and LIDAR in the context of intelligent vehicle. By placing a planar chessboard at different positions and orientations in front of the sensors, the proposed method solves the problem based on 3D reconstruction of the chessboard and geometric constraints between views from the stereovision system and the LIDAR. The three principle steps of the approach are: 3D corner points triangulation, 3D plane least-squares estimation, solving extrinsic parameters by applying a non-linear optimization algorithm based on the geometric constraints. To evaluate the performance of the algorithm, experiments based on computer simulation and real data are performed. The proposed approach is also compared with a popular calibration method to show its advantages.

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

This paper reports the development of a mobile robot for patrol using a single laser range finder. A Laser range finder is useful for outdoor environment regardless of illumination change or various weather conditions. In this paper we combined the motion control of the mobile robot and the algorithm for detecting the outdoor environment. For obstacle avoidance, we adopted the Vector Field Histogram algorithm. A laser range finder is mounted on the mobile robot and looking down the road with a small tilt angle. We propose an algorithm for detecting the surface of the road. The outdoor patrol robot platform is equipped with a DGPS system, a gyro-compass sensor, and a laser range finder. The proposed obstacle avoidance and road detection algorithms were experimentally tested in success.

This paper describes a new method of measuring the position of everyday objects and a robot on the floor using distance and reflectance acquired by laser range finder (LRF). The information obtained by this method is important for a service robot working in a human daily life environment. Our method uses only one LRF together with a mirror installed on the wall. Moreover, since the area of sensing is limited to a LRF scanning plane parallel to the floor and just a few centimeters above the floor, the scanning covers the whole room with minimal invasion of privacy of a resident, and occlusion problem is mitigated by using mirror. We use the reflection intensity and position information obtained from the target surface. Although it is not possible to identify all objects by additionally using reflection values, it would be easier to identify unknown objects if we can eliminate easily identifiable objects by reflectance. In addition, we propose a method for measuring the robot's pose using the tag which has the encoded reflection pattern optically identified by the LRF. Our experimental results validate the effectiveness of the proposed method.

The automated inspection is desired to detect the vehicle in large parking space, which is difficult to survey by fixed the vision-sensor. We purposed mobile robot surveillance in parking space for parking lot inspection. To obtain the parking lot information, vision sensor is wide useful for parking lot state checking which measurement is difficult to obtain the positioning of vehicle while achieved by the laser range finder (LRF). In this research aims a laser range finder is acquired the positioning with directly measure the object in range scanner, which is obtains the data in the parking place. This paper purposed on ability of a LRF sensor with including reflection intensity data for data measuring in the parking lot by using a mobile robot which performs patrolling on multiple times in parking place for a parking lot inspection. We present experimental results to evaluate the effectiveness of the occupancy state recognition.

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.

Developing autonomous mobile robots that can coexist with human in populated environments is still considered a big challenge. To address this problem the authors propose a novel scheme to assist mobile robots by providing localization information externally. This scheme combines the autonomous navigation and target tracking research fields to arrive at a structured assistance system for autonomous mobile robots. In the proposed scheme, the environment is sensed using a laser range finder and camera based sensor unit. Using Rao-Blackwellized particle filter technique, the robots that need assistance are continuously tracked. In contrast with conventional laser range finder based tracking systems, the placement of sensor is changed to a level above average human height and the mobile robots are modified by attaching a cylindrical pole. The experiments show the validity of the proposed scheme for simultaneous localization assistance for multiple mobile robots. Two mobile robots were simultaneously navigated in given trajectories using assistance data, successfully.