Scan Matching Research Articles

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.

Improving Localization Accuracy for an Underwater Robot With a Slow-Sampling Sonar Through Graph Optimization

This paper proposes a novel localization algorithm for an autonomous underwater vehicle equipped with a mechanical scanning sonar that has a slow frequency of data sampling. The proposed approach incrementally constructs a pose graph and conducts graph optimization to correct the robot poses. The construction of a pose graph has three stages: 1) scan generation which incorporates an extended Kalman filter-based dead reckoning algorithm that takes the robot motion into account while eliminating the sonar scan distortion caused by the motion; 2) data association which is based on Mahanalobis distance and shape matching for determining loop closures; and 3) scan matching which calculates constraints constructs pose graph. The constructed pose graph is then fed into a graph optimizer to find the optimal poses corresponding to each scan. A trajectory correction module uses these optimized poses to correct intermediate poses during the process of scan generation. Both simulation and practical experiments are conducted to verify the viability and accuracy of the proposed algorithm.

Accurate Localization for Making Maps to Mobile Robots Using Odometry and GPS Without Scan-Matching

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/11.jpg"" width=""300"" /> Comparison of mapping results</div> This paper studies an accurate localization method to make maps for mobile robots using odometry and a global positioning system (GPS) without scan matching. We investigate requirements for GPS accuracy in map-making. To generate accurate maps, SLAM techniques such as scan matching are used to obtain accurate positions. Scan matching is unstable, however, in complex environments and has a high computation cost. To avoid these problems, we studied accurate localization without scan matching. Loop closing is an important property in generating consistent maps. Inconsistencies in maps prevent correct routes to destinations from being generated. Basically, our method adds scan data to a map along a trajectory given by odometry. Odometry accumulates errors due, e.g., to wheel slippage or wheel diameter variations. To remove this accumulated error, we used bundle adjustment, introducing two types of processing. The first is a simple manual input moving a robot to a same position at start and end. This is equal that a robot returns to a start position at end. The second process uses a GPS device to improve map accuracy. Results of experiments showed that an accurate map is generated by using wheel-encoder odometry and a low-cost GPS device. Results were evaluated using a real-time kinematic (RTK) GPS device whose accuracy is within a few centimeters. </span>

GEOMETRIC POINT QUALITY ASSESSMENT FOR THE AUTOMATED, MARKERLESS AND ROBUST REGISTRATION OF UNORDERED TLS POINT CLOUDS

Abstract. The faithful 3D reconstruction of urban environments is an important prerequisite for tasks such as city modeling, scene interpretation or urban accessibility analysis. Typically, a dense and accurate 3D reconstruction is acquired with terrestrial laser scanning (TLS) systems by capturing several scans from different locations, and the respective point clouds have to be aligned correctly in a common coordinate frame. In this paper, we present an accurate and robust method for a keypoint-based registration of unordered point clouds via projective scan matching. Thereby, we involve a consistency check which removes unreliable feature correspondences and thus increases the ratio of inlier correspondences which, in turn, leads to a faster convergence of the RANSAC algorithm towards a suitable solution. This consistency check is fully generic and it not only favors geometrically smooth object surfaces, but also those object surfaces with a reasonable incidence angle. We demonstrate the performance of the proposed methodology on a standard TLS benchmark dataset and show that a highly accurate and robust registration may be achieved in a fully automatic manner without using artificial markers.

Efficient scan matching method using direction distribution

An efficient scan matching method that uses direction distributions of two-dimensional laser scans is proposed. Using line fitting with each point in the scan, together with the neighbouring two points, the distribution of these line directions is computed. The direction distribution represents a measure of the geometric tendency of the scan, and this can be used to compute similarities between scans. This approach can be applied to select scans in a scan database that have high probabilities of being closely aligned with the current scan. Moreover, an initial estimate of the rotational displacement between two scans can be obtained, and scan matching can be accomplished efficiently even with a large rotational displacement between scans.

TOWARDS GLOBALLY CONSISTENT SCAN MATCHINGWITH GROUND TRUTH INTEGRATION

Abstract. The scan matching based simultaneous localization and mapping method with six dimensional poses is capable of creating a three dimensional point cloud map of the environment, as well as estimating the six dimensional path that the vehicle has travelled. The essence of it is the registering and matching of sequentially acquired 3D laser scans, while moving along a path, in a common coordinate frame in order to provide 6D pose estimations at the respective positions, as well as create a three dimensional map of the environment. An approach that could drastically improve the reliability of acquired data is to integrate available ground truth information. This paper is about implementing such functionality as a contribution to 6D SLAM (simultaneous localization and mapping with 6 DoF) in the 3DTK – The 3D Toolkit software (Nüchter and Lingemann, 2011), as well as test the functionality of the implementation using real world datasets.

1A1-J07 無限回転機構を有する可搬型三次元レーザスキャナの開発

The conventional 3D-LIDAR was designed for using on robots or vehicles. The 3D-LIDAR is needed to be miniaturized in order to being carried by human for many hours. Therefore, the present author has developed a mobile 3D-LIDAR which has wide view and rotates at high speed. In order to get 3D point cloud of the environment around, it is necessary to synchronize the signal of motor with the signal of scanning laser range sensor. In this way, the author adopts hardware flow control. For the purpose of map generation, scan matching based on occupancy grid maps which is 2D-SLAM is adopted. For the scan matching, 2D point cloud is generated from projection of the 3D point cloud in the specific heights from the floor onto the horizontal plane. Finally, the 3D environmental map is generated by the coordinate transforms of measured 3D point with the obtained sensor position.

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.

Scan matching method using projection in dominant direction of indoor environment

In this paper, we propose a new scan matching method, which uses the feature of structure in an artificial environment where walls are located parallel or perpendicular to one another. This method can precisely obtain the relative pose between two scan data using the dominant direction with a small calculation cost. Efficacy of this method is proved by an experiment conducted in an indoor environment.

A mobile robot based system for fully automated thermal 3D mapping

It is hard to imagine living in a building without electricity and a heating or cooling system these days. Factories and data centers are equally dependent on a continuous functioning of these systems. As beneficial as this development is for our daily life, the consequences of a failure are critical. Malfunctioning power supplies or temperature regulation systems can cause the close-down of an entire factory or data center. Heat and air conditioning losses in buildings lead to a large waste of the limited energy resources and pollute the environment unnecessarily. To detect these flaws as quickly as possible and to prevent the negative consequences constant monitoring of power lines and heat sources is necessary. To this end, we propose a fully automatic system that creates 3D thermal models of indoor environments. The proposed system consists of a mobile platform that is equipped with a 3D laser scanner, an RGB camera and a thermal camera. A novel 3D exploration algorithm ensures efficient data collection that covers the entire scene. The data from all sensors collected at different positions is joined into one common reference frame using calibration and scan matching. In the post-processing step a model is built and points of interest are automatically detected. A viewer is presented that aids experts in analyzing the heat flow and localizing and identifying heat leaks. Results are shown that demonstrate the functionality of the system.

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.

Global localization for mobile robots using reference scan matching

This paper presents a new approach based on scan matching for global localization with a metric-topological hybrid world model. The proposed method aims to estimate relative pose to the most likely reference site by matching an input scan with reference scans, in which topological nodes are used as reference sites for pose hypotheses. In order to perform scan matching we apply the spectral scan matching (SSM) method that utilizes pairwise geometric relationships (PGR) formed by fully interconnected scan points. The SSM method allows the robot to achieve scan matching without using an initial alignment between two scans and geometric features such as corners, curves, or lines. The localization process is composed of two stages: coarse localization and fine localization. Coarse localization with 2D geometric histogram constructed from the PGR is fast, but not precise sufficiently. On the other hand, fine localization using the SSM method is comparatively slow, but more accurate. This coarse-to-fine framework reduces the computational cost, and makes the localization process reliable. The feasibility of the proposed methods is demonstrated by results of simulations and experiments.

2A2-Q01 人混み中での移動ロボットの自己位置推定に有効な測域センサデータ処理法の提案 : 蓄積処理と近傍点削除による壁面の抽出(移動ロボットの自己位置推定と地図構築)

The purpose of this study is to enable a mobile robot to localize itself in a crowded environment. Scan matching is an example of localization method. However, it is not effective in environments with many dynamic obstacles, i.e., crowded environments. In this study, we propose a method to obtain data usable for scan matching in crowded indoor environments by the process of scan data accumulation and near point deletion. With this method, we can obtain wall data that is similar to when there is no crowd. Experiments were carried out to investigate the effectiveness of the method. As a result, the method was found to be effective in localization in environments with many dynamic obstacles.

Robust Robot Navigation using Polar Coordinates in Dynamic Environments

This paper presents robust robot navigation using polar coordinates in dynamic environments based on our previous work which was to move toward a target while avoiding static and dynamic obstacles. It utilized vector polar histogram (VPH) with velocity obstacle (VO) for the navigation and obstacle avoidance but had difficulties in detecting dynamic obstacles and reaching the target accurately. To overcome those problems, polar scan matching (PSM) is combined with our previous work. It allows the robot to localize its pose more correctly, which causes an improvement of dynamic obstacle detection. Simulations are performed to verify that our approach outperforms other conventional navigation methods in terms of obstacle avoidance and reaching the target accurately.

Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System

Indoor localization and mapping is an important problem with many applications such as emergency response, architectural modeling, and historical preservation. In this paper, we develop an automatic, off-line pipeline for metrically accurate, GPS-denied, indoor 3D mobile mapping using a human-mounted backpack system consisting of a variety of sensors. There are three novel contributions in our proposed mapping approach. First, we present an algorithm which automatically detects loop closure constraints from an occupancy grid map. In doing so, we ensure that constraints are detected only in locations that are well conditioned for scan matching. Secondly, we address the problem of scan matching with poor initial condition by presenting an outlier-resistant, genetic scan matching algorithm that accurately matches scans despite a poor initial condition. Third, we present two metrics based on the amount and complexity of overlapping geometry in order to vet the estimated loop closure constraints. By doing so, we automatically prevent erroneous loop closures from degrading the accuracy of the reconstructed trajectory. The proposed algorithms are experimentally verified using both controlled and real-world data. The end-to-end system performance is evaluated using 100 surveyed control points in an office environment and obtains a mean accuracy of 10 cm. Experimental results are also shown on three additional datasets from real world environments including a 1500 meter trajectory in a warehouse sized retail shopping center.

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.

Hierarchical Map Building Based UKF-SLAM Approach for AUV

A layered simultaneous localization and mapping (SLAM) approach for building hierarchical maps is proposed in situations that obstacles, environmental sensors and Autonomous Underwater Vehicle (AUV) poses are all unknown in large-scale environments. Firstly, a two-layered environment model is established, which is composed to a submap layer and a topological node layer. Then the perception information of the environmental sensors is employed to assist the online creation and update of submaps local coordinate framework. While building the topological structure, the coupling summation equation is utilized to compute the relative positions between nodes, Based on the loop-closure detection result, the weighted scan matching algorithm and the relaxation algorithm are introduced to optimize the topological structure, which ensures global consistency of the map. Experimental results validate the feasibility and effectiveness of the approach.