2D Laser Range Finder Research Articles

1P1-L02 移動ロボットのための複数の距離センサを用いた段差を含む屋内環境認識(移動ロボットのための視覚)

This paper describes a map generation method for recognizing an indoor scene by using multiple laser range finders (LRFs) to prevent the robot from colliding with obstacles and falling. Many of existing methods use only horizontal-scanning LRFs for mapping, but they cannot recognize small bumps and downstairs. We therefore propose a method of generating a map by using multiple 2D LRF pointing at different directions, by which enough information for a safe robot navigation is provided. One horizontally-scanning LRF measures the distance to surrounding walls, while two downward-looking LRFs detect bumps based on a k-curvature based corner detection in LRF scan data and downstairs based on the deviation from the predicted floor height. Detected bumps and downstairs are added to the usual 2D map generated by using a horizontal-scanning LRF. We validated the proposed method both in an environmental simulator for robotics and in an actual indoor environment.

A Minimal Solution for the Extrinsic Calibration of a Camera and a Laser-Rangefinder

This paper presents a new algorithm for the extrinsic calibration of a perspective camera and an invisible 2D laser-rangefinder (LRF). The calibration is achieved by freely moving a checkerboard pattern in order to obtain plane poses in camera coordinates and depth readings in the LRF reference frame. The problem of estimating the rigid displacement between the two sensors is formulated as one of registering a set of planes and lines in the 3D space. It is proven for the first time that the alignment of three plane-line correspondences has at most eight solutions that can be determined by solving a standard p3p problem and a linear system of equations. This leads to a minimal closed-form solution for the extrinsic calibration that can be used as hypothesis generator in a RANSAC paradigm. Our calibration approach is validated through simulation and real experiments that show the superiority with respect to the current state-of-the-art method requiring a minimum of five input planes.

Object recognition using laser range finder and machine learning techniques

In recent years, computer vision has been widely used on industrial environments, allowing robots to perform important tasks like quality control, inspection and recognition. Vision systems are typically used to determine the position and orientation of objects in the workstation, enabling them to be transported and assembled by a robotic cell (e.g. industrial manipulator). These systems commonly resort to CCD (Charge-Coupled Device) Cameras fixed and located in a particular work area or attached directly to the robotic arm (eye-in-hand vision system). Although it is a valid approach, the performance of these vision systems is directly influenced by the industrial environment lighting. Taking all these into consideration, a new approach is proposed for eye-on-hand systems, where the use of cameras will be replaced by the 2D Laser Range Finder (LRF). The LRF will be attached to a robotic manipulator, which executes a pre-defined path to produce grayscale images of the workstation. With this technique the environment lighting interference is minimized resulting in a more reliable and robust computer vision system. After the grayscale image is created, this work focuses on the recognition and classification of different objects using inherent features (based on the invariant moments of Hu) with the most well-known machine learning models: k-Nearest Neighbor (kNN), Neural Networks (NNs) and Support Vector Machines (SVMs). In order to achieve a good performance for each classification model, a wrapper method is used to select one good subset of features, as well as an assessment model technique called K-fold cross-validation to adjust the parameters of the classifiers. The performance of the models is also compared, achieving performances of 83.5% for kNN, 95.5% for the NN and 98.9% for the SVM (generalized accuracy). These high performances are related with the feature selection algorithm based on the simulated annealing heuristic, and the model assessment (k-fold cross-validation). It makes possible to identify the most important features in the recognition process, as well as the adjustment of the best parameters for the machine learning models, increasing the classification ratio of the work objects present in the robot's environment. Graphical abstractDisplay Omitted Highlights? This paper analyses the performance of laser range finder for object recognition. ? Comparison between laser range finder and CCD camera approach. ? Object classification performed by using machine learning techniques. ? The proposed technique proves to be superior in terms of robustness and reliability.

How the Location of the Range Sensor Affects EKF-based Localization

The article proposes a solution to map-based self-localization for an autonomous robot operating in cluttered and crowded environments. To detect features for localization, 2D laser range-finders traditionally scan a plane parallel to the floor. This work hypothesizes the existence of a "low frequency cross-section" of the 3D Workspace where cluttered and dynamic environments become "more regular" and "less dynamic". The contribution of the article is twofold. First, an "unevenness index" U is introduced to quantitatively measure the complexity of the environment as it would be perceived if the laser range-finder were located at different heights from the floor. The article shows that, by choosing the laser scanning plane to statistically minimize U (in most cases, above the heads of people), it is possible to deal more efficiently with non-linearities in the measurement model, moving objects and occluded features. Second, it is demonstrated that, when adopting an extended Kalman filter for position tracking (a very common and widely used technique in real-world scenarios), the a posteriori covariance of the estimated robot pose converges faster, on average, when U is lower, which leads to better localization performance. Experimental results show hours of continuous robot operation in real-world, cluttered and crowded environments.

A Review of Photo Sensor Laser Range Finder Hokuyo URG-04LX-UG01 Applications

Laser sensor such as laser range finder (LRF) become more and more famous nowadays, many researchers has used, developed and integrate the laser sensor inside their system to achieve their goals. In this paper, we use one of the flagship products of Hokuyo companies, especially their photo sensor/SOKUIKI sensor or known as 2D LRF sensor, a URG-04LX-UG01. Here we review the most recent application on a research work that uses URG-04LX-UG01 since this product launched in 2010. In point of view of interface, sensor combination, environment of use, and functionality of the sensor itself is how we deliver the review. The uniqueness of the application applied is how we assess the research work.

2A1-K11 車輪型倒立振子ロボットのための周辺環境の地図生成 : 三次元距離センサを用いた占有格子地図生成の基礎的検討(移動ロボットの自己位置推定と地図構築(1))

This research aims to develop the mapping of a wheeled inverted pendulum type robot. In this paper, initial work on the generation of a 3D map of an indoor environment using a 3D laser range finder and occupancy grid mapping is presented. From the generated map the structure of the indoor environment was represented. It is suggested that the 3D laser range finder can be used to generate a precise 3D map of an indoor environment.

Robust Registration of Narrow-Field-of-View Range Images

Abstract This paper focuses on range image registration for robot localization and environment mapping. It extends the well-known Iterative Closest Point (ICP) algorithm in order to deal with erroneous measurements. The dealing with measurement errors originating from external lighting, occlusions or limitations in the measurement range is only rudimentary in literature. In this context we present a non-parametric extension to the ICP algorithm that is derived directly from measurement modalities of sensors in projective space. We show how aspects from reverse calibration can be embedded in search-tree-based approaches. Experiments demonstrate the applicability to range sensors like the Kinect device, Time-of-Flight cameras and 3D laser range finders. As a result the image registration becomes faster and more robust.

1A2-F06 2D obstacle avoidance algorithm for mobile robots(Wheeled Robot/Tracked Vehicle(2))

This paper discusses a 2-dimentional obstacle avoidance algorithm for mobile robots who search the environment obstacles by laser range finder. This algorithm finds the most obstacle free path that will allow a robot to move the furthest distance. In this algorithm, width of the robot is taken in to account when determining the obstacle free path for the robot. 2D laser range finder scans the horizontal data of the environment and algorithm finds the maximum travelable direction which covers span of 180° in front of the robot. The effectiveness of the algorithm is validated using MATLAB simulation framework using real environmental data captured by laser range finder.

카메라와 2차원 레이저 거리센서를 활용한 비포장 도로 환경에서의 지상무인차량의 주행가능영역 추정 기법

Unmanned ground vehicle for facility protection mostly uses model of territory for autonomous navigation. However, modeling of territory using several sensors is highly time consuming and sometimes inefficient for road application. Therefore, an estimation of drivable path based on features of road is required for high speed autonomous navigation on road. In this paper, an estimation method of drivable path using camera and 2D laser rangefinder is proposed. First, a vanishing point is estimated based on image data from CCD camera. Second, a road width is estimated based on range data from 2D laser rangefinder. Finally, the drivable path is estimated by fusing the vanishing point and the road width. The proposed method is tested on both well-structured road and unpaved road like cross-country situation.

Localization algorithm using a virtual label for a mobile robot in indoor and outdoor environments

Localization for autonomous mobile robot is one of the most important factors. If current position of robot is unknown, it should be impossible to solve a problem such as how to decide and where to go. There are various localization methods developed by previous researches; localization using RFID tag, ultrasonic and image processing and recognizing landmark. However, these methods have problems and faults. In indoor, RFID, vision and ultrasonic sensors are only detected a restricted space and easily effected by height, interval and direction of installed sensors. In outdoor, it is difficult to detect current position because of disconnected signal and error of GPS which has used in the most of research. This paper proposes new localization algorithm which is assigned virtual label along with map building through information acquired by using a 2D laser range finder, an encoder and a GPS in indoor/outdoor environments. The proposed algorithm for virtual label is verified by applying a mobile robot both indoor and outdoor.

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.

Extrinsic Calibration of a 3D Laser Scanner and an Omnidirectional Camera

We propose an approach for external calibration of a 3D laser scanner with an omnidirectional camera system. The utility of an accurate calibration is that it allows for precise co-registration between the camera imagery and the 3D point cloud. This association can be used to enhance various state of the art algorithms in computer vision and robotics. The extrinsic calibration technique used here is similar to the calibration of a 2D laser range finder and a single camera as proposed by Zhang (2004), but has been extended to the case where we have a 3D laser scanner and an omnidirectional camera system. The procedure requires a planar checkerboard pattern to be observed simultaneously from the laser scanner and the camera system from a minimum of 3 views. The normal of the planar surface and 3D points lying on the surface constrain the relative position and orientation of the laser scanner and the omnidirectional camera system. These constraints can be used to form a non-linear optimization problem that is solved for the extrinsic calibration parameters and the covariance associated with the estimated parameters. Results are presented for a real world data set collected by a vehicle mounted with a 3D laser scanner and an omnidirectional camera system.

Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts

This paper presents a novel vision-based global localization that uses hybrid maps of objects and spatial layouts. We model indoor environments with a stereo camera using the following visual cues: local invariant features for object recognition and their 3D positions for object pose estimation. We also use the depth information at the horizontal centerline of image where the optical axis passes through, which is similar to the data from a 2D laser range finder. This allows us to build our topological node that is composed of a horizontal depth map and an object location map. The horizontal depth map describes the explicit spatial layout of each local space and provides metric information to compute the spatial relationships between adjacent spaces, while the object location map contains the pose information of objects found in each local space and the visual features for object recognition. Based on this map representation, we suggest a coarse-to-fine strategy for global localization. The coarse pose is estimated by means of object recognition and SVD-based point cloud fitting, and then is refined by stochastic scan matching. Experimental results show that our approaches can be used for an effective vision-based map representation as well as for global localization methods.

Simultaneous Localization and Map Building with Modified System State

The full covariance solution to simultaneous localization and map building based on extended Kalman filter requires update time quadratic in the number of landmarks in the map. In order to improve the computational efficiency, this paper reorganizes system state vector and system models. The state of mobile robot is redefined and represented indirectly. The higher dimensional system models and covariance matrix can be represented with two lower dimensional submatrices. An optimization solution is proposed based on this property. The computational requirement and memory requirement are decreased by half. The covariance matrix is fully updated without any approximation during estimation. The optimization solution is consistent and convergent theoretically and realistically. The experiment also compares the performance of optimization solution with the full covariance solution. All these techniques have been implemented on mobile robot ATRVII equipped with 2D laser rangefinder SICK.

Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation

This paper proposes a geometrical feature detection system which is to be used with conventional 2D laser range finders. It consists of three main modules: data acquisition and pre-processing, segmentation and landmark extraction and characterisation. The novelty of this system is a new approach for laser data segmentation based on an adaptive curvature estimation. Contrary to other works, this approach divides the laser scan into line and curve segments. Then, these items are used to directly extract several types of landmarks associated with real and virtual features of the environment (corners, center of tree-like objects, line segments and edges). For each landmark, characterisation provides not only the parameter vector, but also complete statistical information, suitable to be used in a localization and mapping algorithm. Experimental results show that the proposed approach is efficient to detect landmarks for structured and semi-structured environments.

3D Reconstruction of Real World Scenes Using a Low-Cost 3D Range Scanner

: This article presents a 3D reconstruction technique for real world environments based on a traditional 2D laser range finder modified to implement a 3D laser scanner. The article describes the mechanical and control issues addressed to physically achieve the 3D sensor used to acquire the data. It also presents the techniques used to process and merge range and intensity data to create textured polygonal models and illustrates the potential of such a unit. The result is a promising system for 3D modeling of real world scenes at a commercial price 10 or 20 times lower than current commercial 3D laser scanners. The use of such a system can simplify measurements of existing buildings and produce easily 3D models and ortophotos of existing structures with minimum effort and at an affordable price.

Symbolic Trajectory Description in Mobile Robotics

Autonomous mobile robot navigation systems are based on three principal kinds of techniques: map-based navigation, map-building-based navigation and mapless navigation. We propose a method for symbolic trajectory description in unknown indoor environments. The chosen form uses a panoramic description called fresco. The method uses distance measurements from a 2D laser range finder, digitises the robot's visibility area, eliminates superfluous data and reorients their presentation. The landmarks are then extracted and organised into the fresco which is validated by means of neighbourhood rules. As the robot moves in the environment, the frescoes are created and both the amount of new information a fresco carries out and its position in relation to the preceding ones are evaluated by means of two criteria. Only frescoes selected as enough informative are stored to describe the robot's route.

2P1-C30 二次元レーザースキャナーを用いた高速移動体の自己位置推定と環境地図の構築

2P1-C30 二次元レーザースキャナーを用いた高速移動体の自己位置推定と環境地図の構築

Symbolic environment representation by means of frescoes in mobile robotics

Autonomous mobile robot navigation systems are based on three principal kinds of techniques: map-based navigation, map-building-based navigation and mapless navigation. We propose a mapless method for trajectory description in unknown indoor environments. The method uses distance measurements from a 2D laser range finder, digitises the robot's visibility area, eliminates superfluous data and reorients their presentation with laws similar to those used in cellular automata. The landmarks are extracted and organised in a panoramic description called fresco. The frescoes which are validated by means of neighbourhood rules. The most informative frescoes are detected by means of two criteria and stored. The stored frescoes are considered as a human-like descritption of the robot's route and could be used by the robot to retrieve its route to its starting point.

Line Extraction in 2D Range Images for Mobile Robotics

This paper presents a geometrical feature detection framework for use with conventional 2D laser rangefinders. This framework is composed of three main procedures: data pre-processing, breakpoint d...