Laser Scan Matching Research Articles

ICET Online Accuracy Characterization for Geometry-Based Laser Scan Matching

ICET Online Accuracy Characterization for Geometry-Based Laser Scan Matching

Visual application of navigation framework in cyber-physical system for mobile robot to prevent disease

In this article, we propose the visual application of a navigation framework for a wheeled robot to disinfect surfaces. Since dynamic environments are complicated, advanced sensors are integrated into the hardware platform to enhance the navigation task. The 2D lidar UTM-30LX from Hokuyo attached to the front of the robot can cover a wide scanning area. To provide better results in laser scan matching, an inertial measurement unit was integrated into the robot’s body. The output of this combination feeds into a global costmap for monitoring and navigation. Additionally, incremental encoders that obtain high-resolution position data are connected to the rear wheels. The role of the positioning sensor is to identify the existing location of the robot in a local costmap. To detect the appearance of a human, a Kinect digital camera is fixed to the top of the robot. All feedback signals are combined in the host computer to navigate the autonomous robot. For disinfection missions, the robot must carry several ultraviolet lamps to autonomously patrol in unknown environments. To visualize the robot’s effectiveness, our approach was validated using both a virtual simulation and an experimental test. The contributions of this work are summarized as follows: (i) a structure for ultraviolet-based hardware was first established; (ii) the theoretical computations for the robot’s localization in the 3D workspace will play a fundamental role in further developments; and (iii) data fusion from advanced sensing devices was integrated to enable navigation in uncertain environments.

K-Closest Points and Maximum Clique Pruning for Efficient and Effective 3-D Laser Scan Matching

We propose K-Closest Points (KCP), an efficient and effective laser scan matching approach inspired by LOAM and TEASER++. The efficiency of KCP comes from a feature point extraction approach utilizing the multi-scale curvature and a heuristic matching method based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -closest points. The effectiveness of KCP comes from the integration of the feature point matching approach and the maximum clique pruning. We compare KCP against well-known scan matching approaches on synthetic and real-world LiDAR data (nuScenes dataset). In the synthetic data experiment, KCP-TEASER reaches a state-of-the-art root-mean-square transformation error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(0.006\,\mathrm{m}, 0.014^\circ)$</tex-math></inline-formula> with average computational time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$49\mathrm{ms}$</tex-math></inline-formula> . In the real-world data experiment, KCP-TEASER achieves an average error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(0.018\,\mathrm{m}, 0.101^\circ)$</tex-math></inline-formula> with average computational time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$77\,\mathrm{ms}$</tex-math></inline-formula> . This shows its efficiency and effectiveness in real-world scenarios. Through theoretic derivation and empirical experiments, we also reveal the outlier correspondence penetration issue of the maximum clique pruning that it may still contain outlier correspondences.

RTOB SLAM: Real-Time Onboard Laser-Based Localization and Mapping

RTOB-SLAM is a new low-computation framework for real-time onboard simultaneous localization and mapping (SLAM) and obstacle avoidance for autonomous vehicles. A low-resolution 2D laser scanner is used and a small form-factor computer perform all computations onboard. The SLAM process is based on laser scan matching with the iterative closest point technique to estimate the vehicle’s current position by aligning the new scan with the map. This paper describes a new method which uses only a small subsample of the global map for scan matching, which improves the performance and allows for a map to adapt to a dynamic environment by partly forgetting the past. A detailed comparison between this method and current state-of-the-art SLAM frameworks is given, together with a methodology to choose the parameters of the RTOB-SLAM. The RTOB-SLAM has been implemented in ROS and perform well in various simulations and real experiments.

Multi-data sensor fusion framework to detect transparent object for the efficient mobile robot mapping

PurposeAn efficient perception of the complex environment is the foremost requirement in mobile robotics. At present, the utilization of glass as a glass wall and automated transparent door in the modern building has become a highlight feature for interior decoration, which has resulted in the wrong perception of the environment by various range sensors. The perception generated by multi-data sensor fusion (MDSF) of sonar and laser is fairly consistent to detect glass but is still affected by the issues such as sensor inaccuracies, sensor reliability, scan mismatching due to glass, sensor model, probabilistic approaches for sensor fusion, sensor registration, etc. The paper aims to discuss these issues.Design/methodology/approachThis paper presents a modified framework – Advanced Laser and Sonar Framework (ALSF) – to fuse the sensory information of a laser scanner and sonar to reduce the uncertainty caused by glass in an environment by selecting the optimal range information corresponding to a selected threshold value. In the proposed approach, the conventional sonar sensor model is also modified to reduce the wrong perception in sonar as an outcome of the diverse range measurement. The laser scan matching algorithm is also modified by taking out the small cluster of laser point (w.r.t. range information) to get efficient perception.FindingsThe probability of the occupied cells w.r.t. the modified sonar sensor model becomes consistent corresponding to diverse sonar range measurement. The scan matching technique is also modified to reduce the uncertainty caused by glass and high computational load for the efficient and fast pose estimation of the laser sensor/mobile robot to generate robust mapping. These stated modifications are linked with the proposed ALSF technique to reduce the uncertainty caused by glass, inconsistent probabilities and high load computation during the generation of occupancy grid mapping with MDSF. Various real-world experiments are performed with the implementation of the proposed approach on a mobile robot fitted with laser and sonar, and the obtained results are qualitatively and quantitatively compared with conventional approaches.Originality/valueThe proposed ASIF approach generates efficient perception of the complex environment contains glass and can be implemented for various robotics applications.

Classification of image matching point clouds over an urban area

ABSTRACTAirborne laser scanning (ALS) and image matching are the two main techniques for generating point clouds for large areas. While the classification of ALS point clouds has been well investigated, there are few studies that are related to image matching point clouds. In this study, point clouds of multiple resolutions from high-resolution aerial images (ground sampling distance, GSD, of 6 cm) over the city of Vienna were generated and investigated with respect to point density and processing time. Three different study sites with various urban structures are selected from a bigger dataset and classified based on two different approaches: machine learning and a traditional operator-based decision tree. Classification accuracy was evaluated and compared with confusion matrices. In general, the machine learning method results in a higher overall accuracy compared to the simple decision tree method, with accuracies of 87% and 84%, respectively, at the highest resolution. At lower-resolution levels (GSDs of 12 cm and 24 cm), the overall accuracy of machine learning drops by 4% and that of the simple decision tree by 7% for each level. Classifying rasterized data instead of the original point cloud resulted in an accuracy drop of 5%. Thus, using machine learning on point clouds at the highest available resolution is suggested for classification of urban areas.

激光扫描匹配方法研究综述

激光扫描匹配方法研究综述

A 2.5D Map-Based Mobile Robot Localization via Cooperation of Aerial and Ground Robots.

Recently, there has been increasing interest in studying the task coordination of aerial and ground robots. When a robot begins navigation in an unknown area, it has no information about the surrounding environment. Accordingly, for robots to perform tasks based on location information, they need a simultaneous localization and mapping (SLAM) process that uses sensor information to draw a map of the environment, while simultaneously estimating the current location of the robot on the map. This paper aims to present a localization method based in cooperation between aerial and ground robots in an indoor environment. The proposed method allows a ground robot to reach accurate destination by using a 2.5D elevation map built by a low-cost RGB-D (Red Green and Blue-Depth) sensor and 2D Laser sensor attached onto an aerial robot. A 2.5D elevation map is formed by projecting height information of an obstacle using depth information obtained by the RGB-D sensor onto a grid map, which is generated by using the 2D Laser sensor and scan matching. Experimental results demonstrate the effectiveness of the proposed method for its accuracy in location recognition and computing speed.

Feature-Based Laser Scan Matching and Its Application for Indoor Mapping.

Scan matching, an approach to recover the relative position and orientation of two laser scans, is a very important technique for indoor positioning and indoor modeling. The iterative closest point (ICP) algorithm and its variants are the most well-known techniques for such a problem. However, ICP algorithms rely highly on the initial guess of the relative transformation, which will reduce its power for practical applications. In this paper, an initial-free 2D laser scan matching method based on point and line features is proposed. We carefully design a framework for the detection of point and line feature correspondences. First, distinct feature points are detected based on an extended 1D SIFT, and line features are extracted via a modified Split-and-Merge algorithm. In this stage, we also give an effective strategy for discarding unreliable features. The point and line features are then described by a distance histogram; the pairs achieving best matching scores are accepted as potential correct correspondences. The histogram cluster technique is adapted to filter outliers and provide an accurate initial value of the rigid transformation. We also proposed a new relative pose estimation method that is robust to outliers. We use the lq-norm (0 < q < 1) metric in this approach, in contrast to classic optimization methods whose cost function is based on the l2-norm of residuals. Extensive experiments on real data demonstrate that the proposed method is almost as accurate as ICPs and is initial free. We also show that our scan matching method can be integrated into a simultaneous localization and mapping (SLAM) system for indoor mapping.

Using of Laser Scanning and Dense Stereo Matching for 3D Documentation and Virtual Reconstruction of the Ancient Sama Monastery/ Jordan

Sama Monastery is one of the best preserved architecture built in the third century AD at Jordan’s eastern desert. Before the twentieth century, the roofing materials of Monastery, even most of the arch stones, were carried off for the construction of bridges and ferries above the valleys and water channels in the region. Documenting the Monastery which remains in scientific and precise ways is an important goal in order to protect and preserve this important part of the cultural heritage of Jordan. In this study, laser scanner and digital photogrammetry have been used for this purpose. In spite of the potential of each single approach, the integration aims at supporting the visual quality as well as the geometric accuracy of the collected textured 3D models. The purpose of such visual information will serve as a tool for the identification of the nature, extent and severity of the

ICP Algorithm in Mobile Robot Navigation: Analysis of Computational Demands in Embedded Solutions

Abstract: An accurate robot localization, particularly in indoor environments, represents a challenging aspect in mobile robotics due to lack of computational power of commonly used embedded platforms. To reduce the required computational demands it is necessary to apply an efficient algorithm capable of parallelization. This contribution elaborates on implementation of Iterative Closest Point (ICP) laser scan matching algorithm. A computer program developed and optimized in C# language is compared to a mathematical model designed in MATLAB. The characteristics of both approaches are assessed based on computational demands (e.g. time) and nearest neighbor determination.

NAVIS-An UGV indoor positioning system using laser scan matching for large-area real-time applications.

Laser scan matching with grid-based maps is a promising tool for real-time indoor positioning of mobile Unmanned Ground Vehicles (UGVs). While there are critical implementation problems, such as the ability to estimate the position by sensing the unknown indoor environment with sufficient accuracy and low enough latency for stable vehicle control, further development work is necessary. Unfortunately, most of the existing methods employ heuristics for quick positioning in which numerous accumulated errors easily lead to loss of positioning accuracy. This severely restricts its applications in large areas and over lengthy periods of time. This paper introduces an efficient real-time mobile UGV indoor positioning system for large-area applications using laser scan matching with an improved probabilistically-motivated Maximum Likelihood Estimation (IMLE) algorithm, which is based on a multi-resolution patch-divided grid likelihood map. Compared with traditional methods, the improvements embodied in IMLE include: (a) Iterative Closed Point (ICP) preprocessing, which adaptively decreases the search scope; (b) a totally brute search matching method on multi-resolution map layers, based on the likelihood value between current laser scan and the grid map within refined search scope, adopted to obtain the global optimum position at each scan matching; and (c) a patch-divided likelihood map supporting a large indoor area. A UGV platform called NAVIS was designed, manufactured, and tested based on a low-cost robot integrating a LiDAR and an odometer sensor to verify the IMLE algorithm. A series of experiments based on simulated data and field tests with NAVIS proved that the proposed IMEL algorithm is a better way to perform local scan matching that can offer a quick and stable positioning solution with high accuracy so it can be part of a large area localization/mapping, application. The NAVIS platform can reach an updating rate of 12 Hz in a feature-rich environment and 2 Hz even in a feature-poor environment, respectively. Therefore, it can be utilized in a real-time application.

Digital Aléria: Multiresolution Technologies Arming International Collaboration to Restore the Past and Restart

This paper reports about the last progressing results of our work, raised around the archaeological site of the ancient roman urbs of Aléria in Corsica (F), updated to January 2014. Our plan provides to resume the old excavation usable data, to realize a new survey of the site actual status and to collect all into a new 3D geodatabase, including any kind of historical, documental analog data, all reorganized into dedicated Cloud Platforms and Services for different uses, as a multipath solution to improve scientists' work and to promote tourism, both local and international. The project initially focused on the forum, as the only accessible area, in these days enlarges its plans involving the nearby amphitheater and ramparts, just reorganized after the removal of old roofing. It somehow embraces even the nearby Carcopino Museum and the collected Etruscan and Roman finds as traces of the unruly diggings previously realized, to which we'll attempt to restitute logic and documented belonging. Since the Aléria Carcopino Museum shows important archaeological evidences sharing the same origin with some finds exposed in the National Etruscan Museum of Villa Giulia in Rome, a new initiative was set up in these days by surveying the two museums' buildings, to create 3D georeferenced models as containers for sharing digital multiscale data made by innovative computational photography techniques there applied. This operation is now in place in the Villa Giulia Museum as a prototype to be after web-linked to the same survey model to be realized in Aléria. Leading the whole project is the collaboration agreement between the “Collectivité Territoriale de Corse, secteur Archéologie”, and the Department of “Storia, Disegno e Restauro dell'Architettura” SAPIENZA, Italy, formally signed in year 2013. The process employs many multiresolution surveying technologies: 3D Laser scanning, Topography, GPS and Dense Stereo Matching applied on site, on buildings and objects for manifold purposes; moreover Computational Photography techniques applied on Museum's halls, cabinets and exposed objects, to create digital and interactive exhibitions linking both nations. A GIS and WEBGIS workflow followed, to start collecting new and restored databases to build up a complete 3D geo-database with 3D Web scene to be shared in the GIS online Cloud Platform.

Laser Scan Matching by FAST CVSAC in Dynamic Environment

Localization and mapping are very important for safe movement of robots. One possible way to assist with this functionality is to use laser scan matching. This paper describes a method to implement this functionality. It is based on well-known random sampling and consensus (RANSAC) and iterative closest point (ICP). The proposed algorithm belongs to the class of point to point scan matching approach with its matching criteria rule. The performance of the proposed algorithm is examined in real environment and found applicable in real-time application.

Mapping of Rescue Environment Based on NDT Scan Matching

This paper studied the mapping problem for rescue robots based on laser scan matching and extend Kalman filtering (EKF). Because of the non-structural rescue environments, it is hard to extract typical features. Scan matching method based on normal distribution transform (NDT) can avoid the hard feature extraction problem by estimation of the probability distribution of laser scan data. By fusing NDT scan matching with EKF framework, the NDT-EKF SLAM algorithm was proposed, which can effectively and precisely build maps for rescue environment. Experiment results show that NDT-EKF SLAM algorithm is more precise than algorithms based solely on scan-matching.

Laser-Based Pedestrian Tracking in Outdoor Environments by Multiple Mobile Robots

This paper presents an outdoors laser-based pedestrian tracking system using a group of mobile robots located near each other. Each robot detects pedestrians from its own laser scan image using an occupancy-grid-based method, and the robot tracks the detected pedestrians via Kalman filtering and global-nearest-neighbor (GNN)-based data association. The tracking data is broadcast to multiple robots through intercommunication and is combined using the covariance intersection (CI) method. For pedestrian tracking, each robot identifies its own posture using real-time-kinematic GPS (RTK-GPS) and laser scan matching. Using our cooperative tracking method, all the robots share the tracking data with each other; hence, individual robots can always recognize pedestrians that are invisible to any other robot. The simulation and experimental results show that cooperating tracking provides the tracking performance better than conventional individual tracking does. Our tracking system functions in a decentralized manner without any central server, and therefore, this provides a degree of scalability and robustness that cannot be achieved by conventional centralized architectures.

Inferring laser-scan matching uncertainty with conditional random fields

This paper proposes a novel algorithm for computing robot motion estimates from ranging sensors. The algorithm utilises the recently proposed CRF-Matching procedure which matches laser scans based on shape descriptors. The motion estimates are computed in a sound probabilistic framework by performing inference on a probabilistic graphical model. The Sampling-Product inference algorithm is proposed for obtaining probable association hypothesis from the probabilistic model. The hypothesis are used to generate estimates on the uncertainty of translational and rotational movements of the mobile robot. Experiments demonstrate the benefits of the approach on simulated data sets and on laser scans from an urban environment. The approach is also combined with the well-established delayed-state information filter for a large-scale outdoor simultaneous localisation and mapping task.

Quantification of Relative Movement Uncertainty Based on Laser Scan Matching and Graphical Model for Mobile Robot

摘要: 提出激光束匹配问题中移动机器人相对运动估计误差的分析方法. 以概率图模型建模激光点几何特征, 构建链式成双条件随机场推理激光点关联的边缘概率. 通过对关联配置空间的节点和状态限定, 紧凑采样激光束匹配不确定性概率分布, 并映射于相对运动估计空间, 推理相对运动估计误差. 实验验证了算法的有效性. 关键词: 激光束匹配 / 不确定性估计 / 条件随机场 / 图模型

1P1-E05 マルチ移動ロボットによる屋外環境でのレーザベースト歩行者追跡(3次元計測/センサフュージョン)

This paper presents a laser based pedestrian tracking by multi-mobile robots in outdoor environments. Each robot finds pedestrians from its own laser scan image based on an occupancy-grid-based method, and it tracks the detected pedestrians via Kalman filtering and Global-nearest-neighbor-based data association. The tracking data are broadcasted to multi-robots through intercommunication, and they are blended based on Covariance Intersection. For the pedestrian tracking, each robot identifies its own posture based on RTK-GPS and laser scan matching. By our tracking method, all the robots share the tracking data with each other, so that they can always recognize pedestrians that are invisible to any robots. The experimental results of tracking three pedestrians by three mobile robots validate the proposed method.

Feature-to-Feature based Laser Scan Matching in Polar Coordinates with Application to Pallet Recognition

This paper presents a novel method called feature-to-feature based polar scan matching (FFPSM) for pallet recognition. Since FFPSM works in the laser scanner's polar coordinates, the transformation from polar coordinate to Cartesian coordinate is eliminated and some operations are simplified by taking the advantage of the structure of the laser measurements. The method is evaluated in the simulated laser scans. The research results show that the recognition ratio is the same but the recognition velocity is faster than previous works