Scan Matching Research Articles

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.

Scan matching online cell decomposition for coverage path planning in an unknown environment

This paper presents a novel sensor-based online coverage path-planning algorithm that guarantees the complete coverage of an unknown rectilinear workspace for the task of a mobile robot. The proposed algorithm divides the workspace of the robot into cells at each scan sample. This division can be classified as an exact cell decomposition method, which incrementally constructs cell decomposition while the robot covers an unknown workspace. To guarantee complete coverage, a closed map representation based on a feature extraction that consists of a set of line segments called critical edges is proposed. In this algorithm, cell boundaries are formed by extended critical edges, which are the sensed partial contours of walls and objects in the workspace. The robot uses a laser scanner to sense the critical edges. Sensor measurement is sampled twice in each cell. Scan matching is performed to merge map information between the reference scan and the current scan. At each scan sample, a two-direction oriented rectilinear decomposition is achieved in the workspace and presented by a closed map representation. The construction order of the cells is very important in this incremental cell decomposition algorithm. To choose the next target cell from candidate cells, the robot checks for redundancy in the planned path and for possible positions of the ending points of the current cell. The key point of the algorithm is memorizing the covered space to define the next target cell from possible cells. The path generation within the defined cell is determined to minimize the number of turns, which is the main factor in saving time during the coverage. Therefore, the cell’s long boundary should be chosen as the main path of the robot. This algorithm is verified by an experiment under the LABVIEW environment.

A scan restoration method for robust polar scan matching in dynamic environments

This paper addresses the problem of scan matching which is highly indispensable for mobile robot systems based on range sensors. Recently, polar scan matching (PSM) has been used in solving the problem because it is accurate and fast enough to be performed in real time. However, the performance of PSM degenerates when the portion of scan data from dynamic objects is excessively large. This paper proposes a scan restoration method to overcome this problem and improve the performance of PSM in dynamic environments. The proposed method restores the scan data from dynamic objects to appropriate scan data from static objects. First, whole scan data is segmented and classified as static and dynamic objects. Next, curvature functions are extracted from the classified segments and smoothed by interpolating the segments indicating dynamic obstacles. PSM with the proposed method was tested and evaluated in various real dynamic environments, which reveals that the proposed method can improve the performance of PSM in dynamic environments.

Two different tools for three-dimensional mapping: DE-based scan matching and feature-based loop detection

SUMMARYAn autonomous robot must obtain information about its surroundings to accomplish multiple tasks that are greatly improved when this information is efficiently incorporated into a map. Some examples are navigation, manipulation, localization, etc. This mapping problem has been an important research area in mobile robotics during last decades. It does not have a unique solution and can be divided into multiple sub-problems. Two different aspects of the mobile robot mapping problem are addressed in this work. First, we have developed a Differential Evolution-based scan matching algorithm that operates with high accuracy in three-dimensional environments. The map obtained by an autonomous robot must be consistent after registration. It is basic to detect when the robot is navigating around a previously visited place in order to minimize the accumulated error. This phase, which is called loop detection, is the second aspect studied here. We have developed an algorithm that extracts the most important features from two different three-dimensional laser scans in order to obtain a loop indicator that is used to detect when the robot is visiting a known place. This approach allows the introduction of very different characteristics in the descriptor. First, the surface features include the geometric forms of the scan (lines, planes, and spheres). Second, the numerical features are values that describe several numerical properties of the measurements: volume, average range, curvature, etc. Both algorithms have been tested with real data to demonstrate that these are efficient tools to be used in mapping tasks.

Scan matching SLAM in underwater environments

This paper proposes a pose-based algorithm to solve the full simultaneous localization and mapping problem for autonomous underwater vehicle (AUV) navigating in unknown and possibly unstructured en...

Development of Method Using a Combination of DGPS and Scan Matching for the Making of Occupancy Grid Maps for Localization

This paper describes a method of making an occupancy grid map through the combined use of DGPS and scan matching. In outdoor environments such as city areas, high-accuracy localization is required for autonomous navigation. Scan matching with a laser scanner and an occupancy grid map consisting of precise structure information on the environment is one of the most accurate localization methods. However, mismatching on the map sometimes occurs, resulting in the robot losing its own position. Although a GPS device, an absolute positioning device, is valid for estimating position and attitude to a certain degree of accuracy, GPS often obtains erroneous positions for multipath problems which occur around tall buildings. In order to estimate the position and attitude of robots more stably, the authors have developed a method of making an occupancy grid map, which corresponds to DGPS directions and has an accurate shape, by using of some accurate DGPS measurement points and the SLAM method. In autonomous navigation, the robot trajectory is estimated using the particle filter method, evaluation and resampling are done using the two ways mentioned above, and attitude is calculated using DGPS measurement points and the result of scan matching. In this paper, the performance of the map-making method and localization method for autonomous navigation is shown through experiments which are evaluated as to the accuracy of the map in an actual environment.

A novel efficient algorithm for mobile robot localization

Being autonomous is one of the most important goals in mobile robots. One of the fundamental works to achieve this goal is giving the ability to a robot for finding its own correct position and orientation. Different methods have been introduced to solve this problem. In this paper, a novel method based on the harmony search (HS) algorithm for robot localization through scan matching is proposed. Simulation results show that the proposed method in comparison with a genetic algorithm-based approach has better accuracy and higher performance. Furthermore a new hybrid algorithm based on harmony search and differential evolution (DE) algorithms is proposed and evaluated on different benchmark functions. Finally the hybrid algorithm has been applied for mobile robot localization and it outperformed the HS-based approach.

Dynamic Obstacle Detection Using Visually Informed Scan Matching

This paper presents a real-time dynamic obstacle detection algorithm using a scan matching method considering image information from a mobile robot equipped with a camera and a laser scanner. By combining image and laser scan data, we extract a scan segment corresponding to the dynamic obstacle. To complement the performance of scan matching, poor in dynamic environments, the extracted scan segment is temporarily removed. After obtaining a good robot position, the position of the dynamic obstacle is calculated based on the robot’s position. Through two experimental scenarios, the performance of the proposed algorithm is demonstrated.

Critical Rays Scan Match SLAM

Scan matching is one of the oldest and simplest methods for occupancy grid based SLAM. The general idea is to find the pose of a robot and update its map simply by calculating the 2-D transformation between a laser scan and its predecessor. Due to its simplicity many solutions were proposed and used in various systems, the vast majority of which are iterative. The fact is, that although scan matching is simple in its implementation, it suffers from accumulative noise. Of course, there is certainly a trade-off between the quality of results and the execution time required. Many algorithms have been introduced, in order to achieve good quality maps in a small iteration time, so that on-line execution would be achievable. The proposed SLAM scheme performs scan matching by implementing a ray-selection method. The main idea is to reduce complexity and time needed for matching by pre-processing the scan and selecting rays that are critical for the matching process. In this paper, several different methods of ray-selection are compared. In addition matching is performed between the current scan and the global robot map, in order to minimize the accumulated errors. RRHC (Random Restart Hill Climbing) is employed for matching the scan to the map, which is a local search optimization procedure that can be easily parameterized and is much faster than a traditional genetic algorithm (GA), largely because of the low complexity of the problem. The general idea is to construct a parameterizable SLAM that can be used in an on-line system that requires low computational cost. The proposed algorithm assumes a structured civil environment, is oriented for use in the RoboCup - RoboRescue competition, and its main purpose is to construct high quality maps.

A Robotic Indoor 3D Mapping System Using a 2D Laser Range Finder Mounted on a Rotating Four-Bar Linkage of a Mobile Platform

This paper describes our work in developing a 3D robotic mapping system composed by an experimental mobile platform equipped with a rotating laser range finder (LRF). For the purpose of obtaining more complete 3D scans of the environment, we design, construct and calibrate a crank-rocker four-bar linkage so that a LRF mounted on it could undergo repetitive rotational motion between two extreme positions, allowing both horizontal and vertical scans. To reduce the complexity of map representation suitable for optimization later, the local map from the LRF is a grid map represented by a distance-transformed (DT) matrix. We compare the DT-transformed maps and find the transformation matrix of a robot pose by a linear simplex-based map optimization method restricted to a local region allows efficient alignment of maps in scan matching. Several indoor 2D and 3D mapping experiments are presented to demonstrate the consistency, efficiency and accuracy of the 3D mapping system for a mobile robot that is stationary or in motion.

G151031 車両連結型ロボットの冗長性を生かしたSLAMに関する基礎研究([G151-03]ロボティクスメカトロニクス部門,一般セッション(3))

In this study,we study an articulated vehicle robot which achieves self-localization and mapping on two dimensional space.Each vehicle of the robot equips infrared sensors to measure the distance and encoders to measure the amount of rotation of the crawler.We propose an algorithm to improve the self-localization estimated by odometry.It is realized by using the ICP scan matching algorithm with scaned range data by infrared sensors redundantly.The experimental results shows the validity of the proposed method.

1A2-H07 静的環境における環境地図構築及び歪み補正処理に関する検討(移動ロボットの自己位置推定と地図構築(1))

In late years, the field where robot is used has extended. It becomes active to the development of a robot to use human life space. A map is necessary for the robot move autonomy. However, it is difficult to make various maps such as human life space. In this study, we describe the flow about three kinds of online SLAM. We made the map using the acquired data and compared accuracy. As a result, ICP Scan Matching showed the best result. But it is distorted by the sensor error and modeling error. So, we examined correction method. And we evaluated effective by the experiment.

Map Building of Unknown Environment Using L1-norm, Point-to-Point Metric and Evolutionary Computation

In the present paper, a method for building a map of an unknown environment (SLAM) derived from the ICP algorithm using point-to-point metric is proposed. The polar- scan matching technology is used for estimation of the robot location change between two scans in sequence estimate the correct position of the robot. Since map building i s fairly time- consuming, the algorithm of differential evolution (DE) is used in the calculation. This efficient optimizer provides very good results in different types of small office environm ent (unstructured and structured). The new type of an algorithm for map building is based purely on simple geometric primitives— vectors and integrates the modern evolutionary algorithm—DE. The presented algorithm falls into the wider group of geometric map builders and is able to build a map of indoor, mostly office, environment without moving objects.

One billion points in the cloud – an octree for efficient processing of 3D laser scans

Automated 3-dimensional modeling pipelines include 3D scanning, registration, data abstraction, and visualization. All steps in such a pipeline require the processing of a massive amount of 3D data, due to the ability of current 3D scanners to sample environments with a high density. The increasing sampling rates make it easy to acquire Billions of spatial data points. This paper presents algorithms and data structures for handling these data. We propose an efficient octree to store and compress 3D data without loss of precision. We demonstrate its usage for an exchange file format, fast point cloud visualization, sped-up 3D scan matching, and shape detection algorithms. We evaluate our approach using typical terrestrial laser scans.

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.

Real time egomotion of a nonholonomic vehicle using LIDAR measurements

This paper presents a technique to estimate in real time the egomotion of a vehicle based solely on laser range data. This technique calculates the discrepancy between closely spaced two‐dimensional laser scans due to the vehicle motion using scan matching techniques. The result of the scan alignment is converted into a nonlinear motion measurement and fed into a nonholonomic extended Kalman filter model. This model better approximates the real motion of the vehicle when compared to more simplistic models, thus improving performance and immunity to outliers. The motion estimate is intended to be used for egomotion compensation in a target‐tracking algorithm for situation awareness applications. In this paper, several recent scan matching algorithms were evaluated for their accuracy and computational speed: metric‐based iterative closest point (MbICP), point‐to‐line ICP (PIICP), and polar scan matching. The proposed approach is performed in real time and provides an accurate estimate of the current robot motion. The MbICP algorithm proved to be the most advantageous scan matching algorithm, but it is still comparable to PlICP. The motion estimation algorithm is validated through experimental testing in real world conditions.

3D Multi-Layered Normal Distribution Transform for Fast and Long Range Scan Matching

Simultaneously Localization and Mapping (SLAM) problem requires a sophisticated scan matching algorithm, in which two consecutive point clouds belonging to highly correlated scene are registered by finding the rigid body transformation parameters when an initial relative pose estimate is available. A well-known scan matching method is the Iterative Closest Point (ICP) algorithm, and the basis of the algorithm is the minimization of an error function that takes point correspondences into account. Another 3D scan matching method called Normal Distribution Transform (NDT) has several advantages over ICP such as the surface representation capability, accuracy, and data storage. On the other hand, the performance of the NDT is directly related to the size of the cell, and there is no proved way of choosing an optimum cell size. In this paper, a novel method called Multi-Layered Normal Distribution Transform (ML-NDT) using various cell sizes in a structured manner is introduced. In this structure a number of layers are used, where each layer contains different but regular cell sizes. In the conventional NDT, the score function is chosen as Gaussian probability function which is minimized iteratively by Newton optimization method. However, the ML-NDT score function is described as the Mahalanobis distance function, and in addition to Newton optimization method, Levenberg–Marquardt algorithm is also adapted to the proposed method for this score function. The performance of the proposed method is compared to the original NDT, and the effects of the optimization methods are discussed. Moreover, an important issue in a scan matching algorithms is the subsampling strategy since the point cloud contains huge amount of data which has a non-uniform distribution. Therefore, the application of a sampling strategy is a must for fast and robust scan matching. In the performance analysis, two sampling strategies are investigated which are random sampling and grid based sampling. The method is successfully applied to experimentally obtained datasets, and the results show that ML-NDT with grid based sampling provides a fast and long range scan matching capability.

Correlation-Based Scan Matching Using Ultrasonic Sensors for EKF Localization

This paper presents a localization method for a mobile robot equipped with only low-cost ultrasonic sensors. Correlation-based Hough scan matching was used to obtain the robot’s pose without any predefined geometric features. A local grid map and a sound pressure model of ultrasonic sensors were used to acquire reliable scan results from uncertain and noisy ultrasonic sensor data. The robot’s pose was measured using correlation-based Hough scan matching, and the covariance was calculated. Localization was achieved by fusing the measurements from scan matching with the robot’s motion model through the extended Kalman filter. Experimental results verified the performance of the proposed localization method in a real home environment.

The UspIC: Performing Scan Matching Localization Using an Imaging Sonar

This paper presents a novel approach to localize an underwater mobile robot based on scan matching using a Mechanically Scanned Imaging Sonar (MSIS). When used to perform scan matching, this sensor presents some problems such as significant uncertainty in the measurements or large scan times, which lead to a motion induced distortion. This paper presents the uspIC, which deals with these problems by adopting a probabilistic scan matching strategy and by defining a method to strongly alleviate the motion induced distortion. Experimental results evaluating our approach and comparing it to previously existing methods are provided.

Homotopic Path Planning for an AUV on Maps Improved with Scan Matching

The main contribution of this paper is an application for an Autonomous Underwater Vehicle (AUV) which first improves dead-reckoning navigation through scan matching techniques to build and Occupancy Grid Map (OGM) and then performs path planning to find safe paths towards the goal. The path planning process is guided with homotopy classes, which topologically describe how paths avoid the obstacles. Then, a homotopic path planner called Homotopic Bug (HBug) efficiently computes a feasible path for each homotopy class in the OGM. The application has been tested on an AUV in a controlled unknown scenario.