Two-dimensional Laser Range Finder Research Articles

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

Multiple two-dimensional laser rangefinders (LRFs) are applied in many applications like mobile robotics, autonomous vehicles, and three-dimensional reconstruction. The extrinsic calibration between LRFs is the first step to perform data fusion and practical application. In this paper, we proposed a simple method to calibrate LRFs based on a corner composed of three mutually perpendicular planes. In contrast to other methods that require a special pattern or assistance from other sensors, the trihedron corner needed in this method is common in daily environments. In practice, we can adjust the position of the LRFs to observe the corner until the laser scanning plane intersects with three planes of the corner. Then, we formed a Perspective-Three-Point problem to solve the position and orientation of each LRF at the common corner coordinate system. The method was validated with synthetic and real experiments, showing better performance than existing methods.

A calibration method of dual two-dimensional laser range finders for mobile manipulator

In this article, a method for calibrating the relative position between dual two-dimensional laser range finders is proposed. This relative position is affected by the manufacture or assembling error of mobile platform, and this error could reduce the accuracy of localization. This article focuses on three-degree-of-freedom calibration, that is, one rotational and two translational degrees of freedom. The entire calibration process can be summed up into three steps. The first step is to allow the dual finders to scan one corner at the same time and then extract the parameters of the corner. The second step is to establish a cost function which is established according to the direction vector of the line and the repeatability of the corners. With this function, the genetic algorithm is used to obtain the final calibration result. Moreover, the finder systematic error and the statistical error are also considered into this article. Simulations and experiments are carried out to verify the proposed method.

Continuously tracking of moving object by a combination of ultra-high frequency radio-frequency identification and laser range finder

Due to the unique and contactless way of identification, radio-frequency identification is becoming an emerging technology for objects tracking. As radio-frequency identification does not provide any distance or bearing information, positioning using radio-frequency identification sensor itself is challenging. Two-dimensional laser range finders can provide the distance to the objects but require complicated recognition algorithms to acquire the identity of object. This article proposes an innovative method to track the locations of dynamic objects by combining radio-frequency identification and laser ranging information. We first segment the laser ranging data into clusters using density-based spatial clustering of applications with noise (DBSCAN). Velocity matching–based approach is used to track the location of object when the object is in the radio-frequency identification reading range. Since the radio-frequency identification reading range is smaller than a two-dimensional laser range finder, velocity matching–based approach fails to track location of the object when the radio-frequency identification reading is not available. In this case, our approach uses the clustering results from density-based spatial clustering of applications with noise to continuously track the moving object. Finally, we verified our approach on a Scitos robot in an indoor environment, and our results show that the proposed approach reaches a positioning accuracy of 0.43 m, which is an improvement of 67.6% and 84.1% as compared to laser-based and velocity matching–based approaches, respectively.

Semi-autonomous Collision Avoidance Flight of UAV Under Bridge by Using Two-dimensional Laser Range Finder with Mirrors

Semi-autonomous Collision Avoidance Flight of UAV Under Bridge by Using Two-dimensional Laser Range Finder with Mirrors

ミラー搭載型二次元測域センサを用いたUAVの高度一定飛行システムの開発

This paper proposes constant altitude flight system using two -dimensional laser range finder with mirrors. Recently, unmanned aerial vehicles (UAVs) are expected to perform in the inspection of deteriorated infrastructures such as bridges and tunnels. In the most of such operations, UAVs should be navigated and controlled in GPS-denied environment. This paper describes three dimensional environmental recognition system using two-dimensional laser range finder with mirrors that can scan over horizontal direction and can measure vertical direction by using the change of the laser direction with mirrors. Then, we develop constant altitude flight system with the sensor. This paper proposes altitude estimation method from the point data obtained by the sensor and demonstrates flight experiment using that two-dimensional laser range finder with mirrors. The flight experiment verified the validity of the developed system.

二次元測域センサを用いたクアッドチルトロータUAVの衝突回避飛行システムの開発

二次元測域センサを用いたクアッドチルトロータUAVの衝突回避飛行システムの開発

Flexible extrinsic calibration of a camera and a two-dimensional laser rangefinder with a folding pattern.

This paper proposes a novel folding pattern for flexible extrinsic calibration of a camera and a 2D laser rangefinder (LRF). The proposed pattern consists of multiple foldable facets. By folding and unfolding the facets, we can easily adjust the sizes and expanding angles of the pattern so that it can be observed by sensors with different view angles at different distances. Moreover, the folding pattern can have different numbers of facets such that it can flexibly generate different constraints to simplify the calibration process. The proposed pattern is thus very flexible and practical. Another important feature is that the proposed method does not require computing the metric information of the pattern, such as plane normal and distance, as required in existing methods. Therefore, it can further simplify the calibration process and enhance the calibration accuracy. In this method, we first derived a linear solution, and then refined the solution with the particle swarm optimization (PSO) method by both enforcing the rotation constraint and minimizing the sum of the square of the point-to-line distances. The proposed calibration pattern and method have been validated with both simulation and real data. Experimental results show that the method is very flexible and accurate for the extrinsic calibration of a camera and a 2D LRF.

Two-dimensional laser feature extraction based on improved successive edge following

Successive edge following (SEF) has been widely used to describe the environmental characteristics based on a two-dimensional (2D) laser range finder due to its simplicity. However, the segmentation accuracy of the regular SEF for different distances is very low. And besides that, the regular SEF sometimes fails to characterize the corner features in the continuous segmentation. To solve these problems, we propose an improved SEF approach, which combines 2D polar radius–arc, adaptive threshold in a region to divide different radius data into groups more reasonably. Region growing is also discussed to localize the corner based on variance of segmentation and its fitting. Finally, all features described by line segments are used to build the map of the environment. As compared to the regular SEF, our approach is information-lossless. Our experimental results show that the segmentation accuracy is improved, while the segmentation number is more realistic and the feature detection accuracy is increased, as compared with the regular SEF.

2차원 LRF의 Raw Sensor Data로부터 추출된 다른 타입의 기하학적 특징

This paper describes extraction methods of five different types of geometrical features (line, arc, corner, polynomial curve, NURBS curve) from the obtained raw data by using a two-dimensional laser range finder (LRF). Natural features with their covariance matrices play a key role in the realization of feature-based simultaneous localization and mapping (SLAM), which can be used to represent the environment and correct the pose of mobile robot. The covariance matrices of these geometrical features are derived in detail based on the raw sensor data and the uncertainty of LRF. Several comparison are made and discussed to highlight the advantages and drawbacks of each type of geometrical feature. Finally, the extracted features from raw sensor data obtained by using a LRF in an indoor environment are used to validate the proposed extraction methods.

RANSAC Feature Based Master-Slave Tracking System for Multi-AGV

In order to solve the transportation problem in large aircraft components assembly process, an AGV posture synchronization system is built, which utilizes a two-dimensional laser range finder and adaptive control method. Two-dimensional laser range finder is located in the front of AGV to collect real-time point cloud of environment. After tracking AGV section point cloud, we extract straight lines and turning points using the RANSAC algorithm, and estimate the relative posture accordingly. Then adaptive controller processes the position information to achieve master-slave tracking for multi-AGV. In experiment we used three sets of identical AGV; the average distance error was less than 5mm while the angle error was limited within 0.3 °. The results verified the reliability and practicability of our system, which can meet the requirements for transporting large parts.

Multi-Feature Extraction for Drivable Road Region Detection with a Two-Dimensional Laser Range Finder

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.

Navigating a Mobile Robot by a Traversability Field Histogram

This paper presents an autonomous terrain navigation system for a mobile robot. The system employs a two-dimensional laser range finder (LRF) for terrain mapping. A so-called "traversability field histogram" (TFH) method is proposed to guide the robot. The TFH method first transforms a local terrain map surrounding the robot's momentary position into a traversability map by extracting the slope and roughness of a terrain patch through least-squares plane fitting. It then computes a so-called "polar traversability index" (PTI) that represents the overall difficulty of traveling along the corresponding direction. The PTIs are represented in a form of histogram. Based on this histogram, the velocity and steering commands of the robot are determined. The concept of a virtual valley and an exit condition are proposed and used to direct the robot such that it can reach the target with a finite-length path. The algorithm is verified by simulation and experimental results.

A Novel Filter for Terrain Mapping With Laser Rangefinders

This paper introduces a novel filter for terrain mapping with a two-dimensional laser rangefinder. The filter, called the certainty-assisted spatial (CAS) filter, uses the physical constraints on motion continuity and spatial continuity to identify corrupted pixels and missing data in an elevation map. The filter removes the corrupted pixels, fills in the missing data, and leaves the uncorrupted pixels intact so as to preserve the details of a terrain map. Our extensive indoor and outdoor mapping experiments show the CAS filter's superior performance in erroneous data reduction and map detail preservation over conventional filters.

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

This paper describes a method to build the map of the environment for a given pose of a mobile robot (local map). The robot surrounding information is provided by a two-dimensional laser range finder which scans radially in a plane parallel to the ground. From this information, the local map builder produces a set of (typically) short line segments which can approximate the shape of any kind of environment. This kind of representation adapts to curved profiles and provides an easy and precise model of any type of environment. Major concerns addressed in this paper are robustness against spureous, noise cleaning, range data clustering, and line segment fitting. Real data will be used to demonstrate the performance of the local mapping system.