Scan Matching Algorithm Research Articles

GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach.

Improved KNN Scan Matching for Local Map Classification in Mobile Robot Localisation Application

Localisation is essential for autonomous mobile robot system enabling it to locate itself within its environment. One method to perform localisation is to use scan matching with iteration closest point (ICP) algorithm. However, typical ICP may be prone to inaccuracies in localisation and mapping due to problems associated with laser range data limitation such as overshoot data and blank data. This paper presents the improvement to the above problem by the inclusion of a threshold to the KNN scan matching algorithm during iteration process. The threshold is a percentage of nearest point of incoming input with respected to reference point. Threshold values of 0%, 70% and 90% were tested, and improvements of the classification performance were observed with the increase in the threshold values, with the latter achieving 100% accuracy. This work shows that the use of threshold in scan matching may improve the accuracy of local map classification.

Large-Scale Outdoor SLAM Based on 2D Lidar

For autonomous driving, it is important to navigate in an unknown environment. In this paper, we propose a fully automated 2D simultaneous localization and mapping (SLAM) system based on lidar working in large-scale outdoor environments. To improve the accuracy and robustness of the scan matching module, an improved Correlative Scan Matching (CSM) algorithm is proposed. For efficient place recognition, we design an AdaBoost based loop closure detection algorithm which can efficiently reject false loop closures. For the SLAM back-end, we propose a light-weight graph optimization algorithm based on incremental smoothing and mapping (iSAM). The performance of our system is verified on various large-scale datasets including our real-world datasets and the KITTI odometry benchmark. Further comparisons to the state-of-the-art approaches indicate that our system is competitive with established techniques.

Usability analysis of scan matching techniques for localization of field machinery in avocado groves

When working in agricultural environments, specially in groves with dense foliage, machinery positioning systems might suffer from loss of GNSS (Global Navigation Satellite System) signal. The latter motivated the development of new localization strategies that use the environment information to localize the machinery and thus fulfil the required agricultural task. In this work, the usability of five well known scan matching algorithms as sole localization systems using a 2D LiDAR (Light Detection and Ranging) scanner is tested in an avocado grove. The aim is to show the pros and cons of such techniques when the machinery faces a real agricultural environment: presence of slippage, absence of GNSS signal, non-flat terrains in a non-experimental grove and noisy LiDAR readings. The analysis presented herein concludes with a localization error evaluation when the machinery has to travel through a rough avocado alley, showing that amongst all the techniques implemented, the Probabilistic Iterative Correspondence (PIC) and the Sum of Gaussian Scan Correlation (SGSC) presented the lowest localization estimation error and remained consistent from a localization point of view.

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.

FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders

Simultaneous Localization and Mapping (SLAM) is an active area of robot research. SLAM with a laser range finder (LRF) is effective for localization and navigation. However, commercial robots usually have to use low-cost LRF sensors, which result in lower resolution and higher noise. Traditional scan-matching algorithms may often fail while the robot is running too quickly in complex environments. In order to enhance the stability of matching in the case of large pose differences, this paper proposes a new method of scan-matching mainly based on Fast Fourier Transform (FFT) as well as its application with a low-cost LRF sensor. In our method, we change the scan data within a range of distances from the laser to various images. FFT is applied to the images to determine the rotation angle and translation parameters. Meanwhile, a new kind of feature based on missing data is proposed to determine the rough estimation of the rotation angle under some representative scenes, such as corridors. Finally, Iterative Closest Point (ICP) is applied to determine the best match. Experimental results show that the proposed method can improve the scan-matching and SLAM performance for low-cost LRFs in complex environments.

Implementation of Odometry with EKF in Hector SLAM Methods

Map building for plain spatial soundings, such as a long and straight corridor in simultaneous localization and mapping (SLAM) is a challenging problem because of lacks of distinguishable landmarks. Such an environment is highly possible to induce erroneous mapping results, such as alias problems. This paper presents a scan matching algorithm with odometer prediction using Extended Kalman Filter (EKF) and an optimal path planning based on regression subgoals. The scan matching process can relax the problems of local minima by means of an effective correction in the odometrical information. By iterating odometrical corrections in each step of running motion model, the matching result can be better than one only believes in individual information from scanning or odometry. Meanwhile, an optimal path planning utilizing an A * algorithm with a regression method is introduced to ensure a mobile robot be able to move elaborately around the corner and speed up along a straight line. Experiments in an indoor environment have been conducted to verify the effectiveness and validation of the proposed techniques.

Topological Based Scan Matching – Odometry Posterior Sampling in RBPF Under Kinematic Model Failures

Rao-Blackwellized Particle Filters (RBPF) have been utilized to provide a solution to the SLAM problem. One of the main factors that cause RBPF failure is the potential particle impoverishment. Another popular approach to the SLAM problem are Scan Matching methods, whose good results require environments with lots of information, however fail in the lack thereof. To face these issues, in the current work techniques are presented to combine Rao-Blackwellized particle filters with a scan matching algorithm (CRSM SLAM). The particle filter maintains the correct hypothesis in environments lacking features and CRSM is employed in feature-rich environments while simultaneously reduces the particle filter dispersion. Since CRSM’s good performance is based on its high iteration frequency, a multi-threaded combination is presented which allows CRSM to operate while RBPF updates its particles. Additionally, a novel method utilizing topological information is proposed, in order to reduce the number of particle filter resamplings. Finally, we present methods to address anomalous situations where scan matching can not be performed and the vehicle displays behaviors not modeled by the kinematic model, causing the whole method to collapse. Numerous experiments are conducted to support the aforementioned methods’ advantages.

A LiDAR and IMU Integrated Indoor Navigation System for UAVs and Its Application in Real-Time Pipeline Classification.

Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low-cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors. Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point-to-point scan matching algorithm using measurements from a horizontally scanning primary LiDAR. The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR. Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data. Additionally, this work presents a novel method for its application in the real-time classification of a pipeline in an indoor map by integrating the proposed navigation approach. Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle. The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram. Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real-time application in industrial plant engineering.

IMPROVED REAL-TIME SCAN MATCHING USING CORNER FEATURES

The automation of unmanned vehicle operation has gained a lot of research attention, in the last few years, because of its numerous applications. The vehicle localization is more challenging in indoor environments where absolute positioning measurements (e.g. GPS) are typically unavailable. Laser range finders are among the most widely used sensors that help the unmanned vehicles to localize themselves in indoor environments. Typically, automatic real-time matching of the successive scans is performed either explicitly or implicitly by any localization approach that utilizes laser range finders. Many accustomed approaches such as Iterative Closest Point (ICP), Iterative Matching Range Point (IMRP), Iterative Dual Correspondence (IDC), and Polar Scan Matching (PSM) handles the scan matching problem in an iterative fashion which significantly affects the time consumption. Furthermore, the solution convergence is not guaranteed especially in cases of sharp maneuvers or fast movement. This paper proposes an automated real-time scan matching algorithm where the matching process is initialized using the detected corners. This initialization step aims to increase the convergence probability and to limit the number of iterations needed to reach convergence. The corner detection is preceded by line extraction from the laser scans. To evaluate the probability of line availability in indoor environments, various data sets, offered by different research groups, have been tested and the mean numbers of extracted lines per scan for these data sets are ranging from 4.10 to 8.86 lines of more than 7 points. The set of all intersections between extracted lines are detected as corners regardless of the physical intersection of these line segments in the scan. To account for the uncertainties of the detected corners, the covariance of the corners is estimated using the extracted lines variances. The detected corners are used to estimate the transformation parameters between the successive scan using least squares. These estimated transformation parameters are used to calculate an adjusted initialization for scan matching process. The presented method can be employed solely to match the successive scans and also can be used to aid other accustomed iterative methods to achieve more effective and faster converge. The performance and time consumption of the proposed approach is compared with ICP algorithm alone without initialization in different scenarios such as static period, fast straight movement, and sharp manoeuvers.

IMPROVED REAL-TIME SCAN MATCHING USING CORNER FEATURES

Abstract. The automation of unmanned vehicle operation has gained a lot of research attention, in the last few years, because of its numerous applications. The vehicle localization is more challenging in indoor environments where absolute positioning measurements (e.g. GPS) are typically unavailable. Laser range finders are among the most widely used sensors that help the unmanned vehicles to localize themselves in indoor environments. Typically, automatic real-time matching of the successive scans is performed either explicitly or implicitly by any localization approach that utilizes laser range finders. Many accustomed approaches such as Iterative Closest Point (ICP), Iterative Matching Range Point (IMRP), Iterative Dual Correspondence (IDC), and Polar Scan Matching (PSM) handles the scan matching problem in an iterative fashion which significantly affects the time consumption. Furthermore, the solution convergence is not guaranteed especially in cases of sharp maneuvers or fast movement. This paper proposes an automated real-time scan matching algorithm where the matching process is initialized using the detected corners. This initialization step aims to increase the convergence probability and to limit the number of iterations needed to reach convergence. The corner detection is preceded by line extraction from the laser scans. To evaluate the probability of line availability in indoor environments, various data sets, offered by different research groups, have been tested and the mean numbers of extracted lines per scan for these data sets are ranging from 4.10 to 8.86 lines of more than 7 points. The set of all intersections between extracted lines are detected as corners regardless of the physical intersection of these line segments in the scan. To account for the uncertainties of the detected corners, the covariance of the corners is estimated using the extracted lines variances. The detected corners are used to estimate the transformation parameters between the successive scan using least squares. These estimated transformation parameters are used to calculate an adjusted initialization for scan matching process. The presented method can be employed solely to match the successive scans and also can be used to aid other accustomed iterative methods to achieve more effective and faster converge. The performance and time consumption of the proposed approach is compared with ICP algorithm alone without initialization in different scenarios such as static period, fast straight movement, and sharp manoeuvers.

Localization of a Mobile Robot Using a Laser Range Finder in a Glass-Walled Environment

A laser range finder (LRF) is one of the most reliable and commonly used sensors for the localization of a mobile robot. Many studies have shown that localization schemes using LRFs satisfy practical requirements. However, it is still a challenging problem to employ LRF-based localization schemes in environments surrounded by transparent or reflective objects such as glass walls or mirrors. Because the LRF is an optical sensor, range measurements are affected by various phenomena at the glass wall, such as diffuse reflection, specular reflection, and penetration. Hence, LRF measurements are erroneous in environments surrounded by glass walls. This paper proposes a new strategy for localization using the LRF in a glass-walled indoor environment. Based on the reflective characteristics of a laser beam, we designed a novel scan matching algorithm under the consideration of all candidate distances that can be measured in the direction of the glass wall. Experimental results show that the proposed method can significantly improve the local tracking performance of a mobile robot in glass-walled environments.

VECTOR MAPS IN MOBILE ROBOTICS

The aim of this paper is to provide a brief overview of vector map techniques used in mobile robotics and to present current state of the research in this field at the Brno University of Technology. Vector maps are described as a part of the simultaneous localization and mapping (SLAM) problem in the environment without artificial landmarks or global navigation system. The paper describes algorithms from data acquisition to map building but particular emphasis is put on segmentation, line extraction and scan matching algorithms. All significant algorithms are illustrated with experimental results.

Toward Autonomous Exploration in Confined Underwater Environments

In this field note, we detail the operations and discuss the results of an experiment conducted in the unstructured environment of an underwater cave complex using an autonomous underwater vehicle AUV. For this experiment, the AUV was equipped with two acoustic sonar sensors to simultaneously map the caves' horizontal and vertical surfaces. Although the caves' spatial complexity required AUV guidance by a diver, this field deployment successfully demonstrates a scan-matching algorithm in a simultaneous localization and mapping framework that significantly reduces and bounds the localization error for fully autonomous navigation. These methods are generalizable for AUV exploration in confined underwater environments where surfacing or predeployment of localization equipment is not feasible, and they may provide a useful step toward AUV utilization as a response tool in confined underwater disaster areas.

Key‐layered normal distributions transform for point cloud registration

A new scan matching algorithm is proposed using the concept of key layers. In the conventional multi-layered normal distributions transform (MLNDT), the number of layers and iterations per layer are fixed and mismatches in point clouds occur due to the limited number of optimising iterations per layer. Moreover, the accuracy of registration is low and the number of layers is heuristically determined in MLNDT. The proposed key-layered normal distributions transform (KLNDT) works well with both enhanced success rate and accuracy. It is also possible for KLNDT to register in higher layers than the traditional MLNDT.

INS/GPS/LiDAR Integrated Navigation System for Urban and Indoor Environments Using Hybrid Scan Matching Algorithm.

This paper takes advantage of the complementary characteristics of Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) to provide periodic corrections to Inertial Navigation System (INS) alternatively in different environmental conditions. In open sky, where GPS signals are available and LiDAR measurements are sparse, GPS is integrated with INS. Meanwhile, in confined outdoor environments and indoors, where GPS is unreliable or unavailable and LiDAR measurements are rich, LiDAR replaces GPS to integrate with INS. This paper also proposes an innovative hybrid scan matching algorithm that combines the feature-based scan matching method and Iterative Closest Point (ICP) based scan matching method. The algorithm can work and transit between two modes depending on the number of matched line features over two scans, thus achieving efficiency and robustness concurrently. Two integration schemes of INS and LiDAR with hybrid scan matching algorithm are implemented and compared. Real experiments are performed on an Unmanned Ground Vehicle (UGV) for both outdoor and indoor environments. Experimental results show that the multi-sensor integrated system can remain sub-meter navigation accuracy during the whole trajectory.

2A2-M09 スキャンデータの順序保存統合を用いたCIF-basedスキャンマッチング法

This paper proposes an order-preserving merging method of 2D range scans for enhancing our CIF-based scan matching algorithm which is a global scan matching algorithm using the CIF descriptors and a geometric constraint between keypoints. Our enhanced version of the CIF-based scan matching algorithm can perform global scan matching more robustly in a large cluttered environments without using an initial alignment. Through experiment in real environment, we confirm the validity of our method.

RGB-D DE-based Scan Matching: Exploiting Colour Properties in Registration

Colour plays a fundamental role in the perception process of humans. In robotics, the exploitation of this type of information has become increasingly important in many different tasks. The development of new sensors has made it possible to obtain colour information together with depth information about the environment. We have recently developed a scan matching algorithm based on evolutionary concepts (Differential Evolution). The main objective of this work is to include colour properties in the registration process, studying how colour can be used to improve the scan matching process. In particular, we have designed a filter to extract the most significant points of a RGB-D scan based on the Delta E divergence between neighbours. In addition, colour properties have also been included in the fitness function of the scan matching method. Our approach has been tested in a real environment and the most significant conclusion is the improvement of the algorithm performance when measuring the valley of convergence.

Perimeter-Based Polar Scan Matching (PB-PSM) for 2D Laser Odometry

The paper presents Perimeter-Based Polar Scan Matching (PB-PSM), a new 2D scan matching algorithm. The algorithm favors matches with a larger perimeter overlap between the two input scans, while using a robust cost minimization process (using an adaptive direct search method, made possible due to a linear complexity data association technique). PB-PSM is benchmarked against the previously published PSM and PSM-C algorithms, and numerous realizations of the ICP algorithm. Results for convergence, accuracy, and computational speed are discussed. PB-PSM is employed on several laser scan datasets, both existing and in-house. Quantitative comparison of resulting maps is done using a new metric for evaluating occupancy grid maps accuracy, by calculating the average cell distance from the walls of the true map. The relative importance of each novel contribution is quantified using the new metric. Additional qualitative analysis is provided for previously published and relatively large datasets.

Fast Registration of Three-Dimensional Laser Scans without Initial Guess

State of the art graph-based 3D simultaneous localization and mapping (SLAM) systems are typically divided into the so-called front- and back end. The front end’s task is to align laser scans locally, constructing a graph of the measurements in the process. The back end’s task is to optimize this graph in order to find a maximally consistent configuration of the nodes. The well-known iterative closest point (ICP) algorithm is often used to align pairs of laser scans. As it only provides local convergence, it generally requires an approximate guess in order to determine the correct relative transformation. In practice, this guess is often obtained from a robot’s odometry. However, in some applications this odometry information is not available and a fast initial guess is needed for real-time operation. Existing feature-based approaches are often unsuitable for real-time operation due to their computational requirements. The authors present a featureless algorithm that is able to compute an approximate transformation between two laser scans quickly, serving as initial guess for ICP. This algorithm is an adaptation to 3D of a 2D correlative scan-matching algorithm by Olson and will be evaluated in the context of 3D SLAM. Comparisons are made to results that use features as they are used in image processing to match images. The authors make some plausible and moderate assumptions on the scan acquisition and experimentally show that these are usually met. The experiments show that the presented algorithm is able to align two 3D laser scans quickly and reliably in the context of mobile robotics and stand-alone laser scanning.