Simultaneous Localization And Mapping Technique Research Articles

A new covariance intersection based integrated SLAM framework for 3D outdoor agricultural applications

AbstractThis letter introduces a novel integrated framework for simultaneous localization and mapping (SLAM) tailored for general agricultural applications. The framework combines a cutting‐edge SLAM method, LIO‐SAM, with covariance intersection for sensor fusion. Agricultural robots often operate in unstructured environments with sparse feature points and encounter repeated similar information, such as trees. Therefore, a fusion framework based on 3D SLAM augmented with additional information, such as feature‐independent GPS data, becomes essential. This study proposes an integrated SLAM framework by introducing a convergence strategy based on covariance analysis, incorporating a state‐of‐the‐art 3D SLAM technique. The convergence methods, namely “dynamic weight assignment” and “winner takes all”, are presented alternatively, tailored to seamlessly integrate with the proposed framework. Evaluations using a public dataset and an experiment demonstrate the effectiveness of the approach through numerical analysis and visual representation. The results illustrate that this method surpasses conventional approaches in accurately estimating the robot's position. In the future, this research will focus on automating crop cultivation and harvesting by integrating the proposed system with robot arm control.

Exploiting Radio Fingerprints for Simultaneous Localization and Mapping

Simultaneous localization and mapping (SLAM) is paramount for unmanned systems to achieve self-localization and navigation. It is challenging to perform SLAM in large environments, due to sensor limitations, complexity of the environment, and computational resources. We propose a novel approach for localization and mapping of autonomous vehicles using radio fingerprints, for example wireless fidelity or long term evolution radio features, which are widely available in the existing infrastructure. In particular, we present two solutions to exploit the radio fingerprints for SLAM. In the first solution—namely Radio SLAM, the output is a radio fingerprint map generated using SLAM technique. In the second solution—namely Radio+LiDAR SLAM, we use radio fingerprint to assist conventional LiDAR-based SLAM to improve accuracy and speed, while generating the occupancy map. We demonstrate the effectiveness of our system in three different environments, namely outdoor, indoor building, and semi-indoor environment .

Dynamic Progress Monitoring of Masonry Construction through Mobile SLAM Mapping and As-Built Modeling

Traditional progress monitoring can be inaccurate and time-consuming, potentially causing time delay and cost overrun in construction projects. With development in technology, tools such as cameras, laser scanners, and building information modelling (BIM) have been used to overcome existing problems in the traditional approach. However, noise mitigation, extracting objects of interest from laser point clouds, and detailed progress measurement are problems that still exist. In this study a novel method of construction progress monitoring to measure the progress percentage is presented. The study integrates the simultaneous localization and mapping (SLAM) technique with as-built BIM to gather quick and accurate construction site progress information. The Hausdorff distance is utilized to extract objects of interest and filter out noise from site-scan data. As-built and as-planned BIM models are compared using Python and Dynamo, to obtain progress percentage. A case study was conducted on a residential building located in Sydney, Australia, to validate the application of the developed method. The outcome demonstrates that utilizing the SLAM technique and Hausdorff distance are effective in mitigating noise and extracting objects of interest from site-scan data, respectively. In addition, with an accuracy of 94.67 percent in estimation, the progress percentage was obtained based on material quantities. The obtained progress percentage could also be used in updating construction schedules and assisting decision-making.

Accuracy Evaluation and Branch Detection Method of 3D Modeling Using Backpack 3D Lidar SLAM and UAV-SfM for Peach Trees during the Pruning Period in Winter

In the winter pruning operation of deciduous fruit trees, the number of pruning branches and the structure of the main branches greatly influence the future growth of the fruit trees and the final harvest volume. Terrestrial laser scanning (TLS) is considered a feasible method for the 3D modeling of trees, but it is not suitable for large-scale inspection. The simultaneous localization and mapping (SLAM) technique makes it possible to move the lidar on the ground and model quickly, but it is not useful enough for the accuracy of plant detection. Therefore, in this study, we used UAV-SfM and 3D lidar SLAM techniques to build 3D models for the winter pruning of peach trees. Then, we compared and analyzed these models and further proposed a method to distinguish branches from 3D point clouds by spatial point cloud density. The results showed that the 3D lidar SLAM technique had a shorter modeling time and higher accuracy than UAV-SfM for the winter pruning period of peach trees. The method had the smallest RMSE of 3084 g with an R2 = 0.93 compared to the fresh weight of the pruned branches. In the branch detection part, branches with diameters greater than 3 cm were differentiated successfully, regardless of whether before or after pruning.

Evolution of Simultaneous Localization and Mapping Framework for Autonomous Robotics—A Comprehensive Review

Abstract Autonomous robotics plays a pivotal role to simplify human–machine interaction while meeting the current industrial demands. In that process, machine intelligence plays a dominant role during the decision making in the operational state-space. Primarily, this decision making and control mechanism relies on sensing and actuation. Simultaneous localization and mapping (SLAM) is the most advanced technique that facilitates both sensing and actuation to achieve autonomy for robots. This work aims to collate multidimensional aspects of simultaneous localization and mapping techniques primarily in the purview of both deterministic and probabilistic frameworks. This investigation on SLAM classification is further elaborated into different categories such as feature-based SLAM and optimization-based SLAM. In this work, the chronological evolution of the SLAM technique develops a comprehensive understanding among the concerned research community.

Bridging the Gap Between Visual Servoing and Visual SLAM: A Novel Integrated Interactive Framework

For pose stabilization task of nonholonomic mobile robots, this article proposes a novel integrated interactive framework, bridging the gap between visual servoing and simultaneous localization and mapping (SLAM). The framework consists of two cooperative components, control module for servoing task and SLAM module for feedback signals estimation. In most visual servoing methods, feedback signals for the servoing controller are estimated by means of multiple-view geometry assuming the target scene being always within the camera field of view (FOV). To handle the challenge that the target scene gets out of view during servoing process, the desired image is associated with the initial map by a two-step strategy, and an incremental map is constructed to guarantee available feedback signals estimation. In addition, on the basis of the kinematic model of the mobile robot and velocities designed by the servo controller, the predicted pose is exploited to discard moving objects in the camera FOV, thus making the proposed framework effective in dynamic scenes. Experimental results operated in different scenes without prior information demonstrate the effectiveness of the proposed approach to handle the FOV problem and dynamic scenes. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Traditional visual servoing stabilization approaches usually require that the feature points in the target scene remain within the FOV of the camera for feedback signals calculation, which is often neglected. Motivated by the requirement of continuous feedback signals to the servo controller, the SLAM technique is introduced to relax the FOV constraint during the servoing process. A novel integrated interactive framework is proposed in this article to further increase the applicability of the servoing system in practice, in which the SLAM module is also redesigned for the flexibility in dynamic scenes. The SLAM module provides feedback signals for the servo controller; meanwhile, velocities designed by the servo controller are utilized for the prediction mechanism in the SLAM module to discard features on moving objects. Experiments validate the applicability of the proposed framework in different scenarios.

SLAM for autonomous planetary rovers with global localization

AbstractThis paper describes a novel approach to simultaneous localization and mapping (SLAM) techniques applied to the autonomous planetary rover exploration scenario to reduce both the relative and absolute localization errors, using two well‐proven techniques: particle filters and scan matching. Continuous relative localization is improved by matching high‐resolution sensor scans to the online created local map. Additionally, to avoid issues with drifting localization, absolute localization is globally corrected at discrete times, according to predefined event criteria, by matching the current local map to the orbiter's global map. The resolutions of local and global maps can be appropriately chosen for computation and accuracy purposes. Further, the online generated local map, of the form of a structured elevation grid map, can also be used to evaluate the traversability of the surrounding environment and allow for continuous navigation. The objective of this study is to support long‐range low‐supervision planetary exploration. The implemented SLAM technique has been validated with a data set acquired during a field test campaign performed at the Teide Volcano on the island of Tenerife, representative of a Mars/Moon exploration scenario.

A Socially Aware SLAM Technique Augmented by Person Tracking Module

In recent years the development of Simultaneous Localization and Mapping (SLAM) techniques have enabled social robots to autonomously navigate in routine human workplaces. However, most common SLAM techniques are developed for mapping and localization in static worlds. In this paper, we have developed and analyzed a novel augmentation of the FastSLAM algorithm, including a person tracking module using 2D LiDAR sensor data. Utilizing this module, the SLAM algorithm is capable of filtering measurements coming from walking people who produce noises due to their intrinsic dynamic and unstableness. This augmentation was developed and then tested on our socially assistive mobile robot platform, Arash, while moving in populated environments utilizing Robotic Operating System (ROS) as a middleware. This new approach demonstrated a clearer representation of the mapped environment and therefore more accurate localization and navigation compared to its static SLAM predecessor.

Field distortion compensation for electromagnetic tracking of ultrasound probes with application in high-dose-rate prostate brachytherapy

Purpose: Electromagnetic (EM) tracking of ultrasound (US) probes has been introduced to expand US imaging capabilities and benefit challenging procedures. However, various instruments—including the US probe itself—may introduce dynamic distortions to the EM field, and compromise the EM measurements. Basic filtering methods, such as those provided by manufacturers, are usually inefficient as they do not allow for field distortion compensation. We propose to use a simultaneous localization and mapping (SLAM) algorithm to track the transrectal US (TRUS) probe while dynamically detect, map, and correct the EM field distortions. Methods: Combining the motion model of the tracked probe, the observations made by a few redundant EM sensors, and the field distortions map, the SLAM algorithm relied on an extended Kalman filter (EKF) to estimate the tracking measurements. The SLAM technique was experimentally validated in a brachytherapy suite. Tracking of a TRUS probe was performed by means of an Ascension trakSTAR tracking system and four EM sensors. In addition, an optical tracking system was employed to provide a ground truth to our data. The performance of the SLAM technique was analysed by varying pertinent parameters, such as the number of redundant measurements and the motion trajectory. Probe trajectories included longitudinal translation, rotation, and freehand motions (consisting of simultaneous longitudinal translation and rotation motions) in order to comprehensively simulate imaging scenarios. Finally, the accuracy of the SLAM estimations was compared with that of the standard filtering methods provided by the manufacturer, as well as that of a simpler sensor fusion technique. Results: SLAM efficiently reduced position tracking errors up to 46.4% during freehand motions of the TRUS probe. Moreover, higher SLAM estimation accuracies were observed as the number of redundant measurements increased. While both TRUS probe motions did not yield a clinically significant trend on position tracking accuracy, orientation measurements were considerably improved during translation of the TRUS probe. Conclusions: The SLAM technique was effective in increasing the tracking accuracy of the TRUS probe. Higher number of redundant sensors and favorable sensor configurations improved the SLAM estimations of EM measurements. In turn, SLAM can further encourage the introduction of EM tracking assistance in clinical procedures such as prostate brachytherapy.

Linear SLAM: Linearising the SLAM problems using submap joining

The main contribution of this paper is a new submap joining based approach for solving large-scale Simultaneous Localization and Mapping (SLAM) problems. Each local submap is independently built using the local information through solving a small-scale SLAM; the joining of submaps mainly involves solving linear least squares and performing nonlinear coordinate transformations. Through approximating the local submap information as the state estimate and its corresponding information matrix, judiciously selecting the submap coordinate frames, and approximating the joining of a large number of submaps by joining only two maps at a time, either sequentially or in a more efficient Divide and Conquer manner, the nonlinear optimization process involved in most of the existing submap joining approaches is avoided. Thus the proposed submap joining algorithm does not require initial guess or iterations since linear least squares problems have closed-form solutions. The proposed Linear SLAM technique is applicable to feature-based SLAM, pose graph SLAM and D-SLAM, in both two and three dimensions, and does not require any assumption on the character of the covariance matrices. Simulations and experiments are performed to evaluate the proposed Linear SLAM algorithm. Results using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization algorithm started from an accurate initial guess. The C/C++ and MATLAB source codes of Linear SLAM are available on OpenSLAM.

Dynamic adaptive simultaneous localization and mapping technique for scene change

How to make simultaneous localization and mapping (SLAM) system to be robust and reliable in complex application scenarios has been widely regarded as a key part of automatic and practical robots. Robustness of SLAM system is one of the research focuses in SLAM field. To tackle this issue, we present multi-scene SLAM technique in this paper, which is a novel extensible SLAM framework by combing different SLAM systems facilitated by a scene detection method. Firstly, we propose a robust and extensible SLAM framework called SceneSLAM to enhance the self-adaptive performance of current SLAM systems. By model structure design, it is easy to utilize open-source SLAM systems to be SLAM models of SceneSLAM and modify the scene detection models to support complex application scenarios. Based on the scene detection results, it can schedule the SLAM models automatically. Secondly, aiming at indoor, outdoor and dark scene detection problems, we present a scene detection model based on convolutional neural network and Bias filter optimization. Experimental results show that the proposed scene detection model has reliable accuracy and stability in detecting indoor, outdoor and dark scenes. What’s more, considering sensor failure in indoor, outdoor and dark scenes, we design dynamic adaptive SLAM models to handle the issue. Map fusion is performed to achieve globally consistent localization and mapping when SLAM models scheduled. At last but not least, we build up a prototype system based on SceneSLAM to enhance the reliability of existing SLAM systems when the scene changes between indoor, outdoor and dark scenes. This system runs on a TurtleBot robot equipped with a Kinect sensor. The experimental results show that multi-scene SLAM technique proposed in this paper provides an effective solution to enhance the robustness of existing SLAM systems in dynamic environments.

An Integrated GNSS/INS/LiDAR-SLAM Positioning Method for Highly Accurate Forest Stem Mapping

Forest mapping, one of the main components of performing a forest inventory, is an important driving force in the development of laser scanning. Mobile laser scanning (MLS), in which laser scanners are installed on moving platforms, has been studied as a convenient measurement method for forest mapping in the past several years. Positioning and attitude accuracies are important for forest mapping using MLS systems. Inertial Navigation Systems (INSs) and Global Navigation Satellite Systems (GNSSs) are typical and popular positioning and attitude sensors used in MLS systems. In forest environments, because of the loss of signal due to occlusion and severe multipath effects, the positioning accuracy of GNSS is severely degraded, and even that of GNSS/INS decreases considerably. Light Detection and Ranging (LiDAR)-based Simultaneous Localization and Mapping (SLAM) can achieve higher positioning accuracy in environments containing many features and is commonly implemented in GNSS-denied indoor environments. Forests are different from an indoor environment in that the GNSS signal is available to some extent in a forest. Although the positioning accuracy of GNSS/INS is reduced, estimates of heading angle and velocity can maintain high accurate even with fewer satellites. GNSS/INS and the LiDAR-based SLAM technique can be effectively integrated to form a sustainable, highly accurate positioning and mapping solution for use in forests without additional hardware costs. In this study, information such as heading angles and velocities extracted from a GNSS/INS is utilized to improve the positioning accuracy of the SLAM solution, and two information-aided SLAM methods are proposed. First, a heading angle-aided SLAM (H-aided SLAM) method is proposed that supplies the heading angle from GNSS/INS to SLAM. Field test results show that the horizontal positioning accuracy of an entire trajectory of 800 m is 0.13 m and is significantly improved (by 70%) compared to that of a traditional GNSS/INS; second, a more complex information added SLAM solution that utilizes both heading angle and velocity information simultaneously (HV-aided SLAM) is investigated. Experimental results show that the horizontal positioning accuracy can reach a level of six centimetres with the HV-aided SLAM, which is a significant improvement (by 86%). Thus, a more accurate forest map is obtained by the proposed integrated method.

COP-SLAM: Closed-Form Online Pose-Chain Optimization for Visual SLAM

In this paper, we analyze and extend the recently proposed closed-form online pose-chain simultaneous localization and mapping (SLAM) algorithm. Pose-chains are a specific type of extremely sparse pose-graphs and a product of contemporary SLAM front-ends, which perform accurate visual odometry and reliable appearance-based loop detection. They are relevant for challenging robotic applications in large-scale 3-D environments for which frequent loop detection is not desired or not possible. Closed-form online pose-chain SLAM efficiently and accurately optimizes pose-chains by exploiting their Lie group structure. The convergence and optimality properties of this solution are discussed in detail and are compared against state-of-the-art iterative methods. We also provide a novel solution space, that of similarity transforms, which has not been considered earlier for the proposed algorithm. This allows for closed-form optimization of pose-chains that exhibit scale drift, which is important to monocular SLAM systems. On the basis of extensive experiments, specifically targeting 3-D pose-chains and using a total of 60 km of challenging binocular and monocular data, it is shown that the accuracy obtained by closed-form online pose-chain SLAM is comparable with that of state-of-the-art iterative methods, while the time it needs to compute its solution is orders of magnitudes lower. This novel SLAM technique thereby is relevant to a broad range of robotic applications and computational platforms.

Geomagnetic field-based localization with bicubic interpolation for mobile robots

This paper proposes a novel geomagnetic field-based SLAM (simultaneous localization and mapping) technique for application to mobile robots. SLAM is an essential technique for mobile robots such as robotic vacuum cleaners to perform their missions autonomously. For practical application to commercialized robotic vacuum cleaners, the SLAM techniques should be implemented with low-priced sensors and low-computational complexity. Most building structures produce distortions in the geomagnetic field and variation of the field over time occurs with extremely low frequency. The geomagnetic field is hence applicable to mobile robot localization. The proposed geomagnetic field SLAM uses only the geomagnetic field signals and odometry data to estimate the robot state and the geomagnetic field signal distribution with low computational cost. To estimate the signal strength of the geomagnetic field, bicubic interpolation, an extension of cubic interpolation for interpolating surfaces on a regular grid, is used. The proposed approach yields excellent results in simulations and experiments in various indoor environments.

A Sensor Fusion Layer to Cope with Reduced Visibility in SLAM

Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained.

Performance analysis of the Microsoft Kinect sensor for 2D Simultaneous Localization and Mapping (SLAM) techniques.

This paper presents a performance analysis of two open-source, laser scanner-based Simultaneous Localization and Mapping (SLAM) techniques (i.e., Gmapping and Hector SLAM) using a Microsoft Kinect to replace the laser sensor. Furthermore, the paper proposes a new system integration approach whereby a Linux virtual machine is used to run the open source SLAM algorithms. The experiments were conducted in two different environments; a small room with no features and a typical office corridor with desks and chairs. Using the data logged from real-time experiments, each SLAM technique was simulated and tested with different parameter settings. The results show that the system is able to achieve real time SLAM operation. The system implementation offers a simple and reliable way to compare the performance of Windows-based SLAM algorithm with the algorithms typically implemented in a Robot Operating System (ROS). The results also indicate that certain modifications to the default laser scanner-based parameters are able to improve the map accuracy. However, the limited field of view and range of Kinect's depth sensor often causes the map to be inaccurate, especially in featureless areas, therefore the Kinect sensor is not a direct replacement for a laser scanner, but rather offers a feasible alternative for 2D SLAM tasks.

Conjugate Unscented FastSLAM for Autonomous Mobile Robots in Large-Scale Environments

Map learning and self-localization based only on a perception of an environment’s structure are fundamental cognitive capacities required for intelligent robot’s to realize true autonomy. Simultaneous localization and mapping (SLAM) is an effective technique for such robots, as it addresses the problem of incrementally building an environment map from noisy sensory data and tracking the robot’s pose with the built map. While the Rao-Blackwellized particle filter (RBPF) is a popular SLAM technique, it tends to accumulate errors introduced by inaccurate linearization of the SLAM nonlinear function. Accordingly, RBPF-SLAM will usually fail to close large loops when applied to large-scale environments. To overcome this drawback, a new Jacobian-free RBPF-SLAM algorithm is derived in this paper. The main contribution of the algorithm lies in the utilization of the 5th-order conjugate unscented transform, which calculates the SLAM transition density up to the 5th order, to give a better distribution of the particle filter and discover local features and landmarks. The performance of the proposed SLAM is investigated and compared with that of FastSLAM2.0 and UFastSLAM in both indoor and outdoor experiments. The results verify that the proposed algorithm improves the SLAM performance in large-scale environments.

A featureless approach to efficient bathymetric SLAM using distributed particle mapping

AbstractThis paper presents an approach to simultaneous localization and mapping (SLAM) suitable for efficient bathymetric mapping that does not require explicit identification, tracking, or association of seafloor features. This is accomplished using a Rao–Blackwellized particle filter, in which each particle maintains a hypothesis of the current vehicle state and a grid‐based, two‐dimensional depth map, efficiently stored by exploiting redundancies between different maps. Distributed particle mapping is employed to remove the computational expense of map copying during the resampling process. The proposed approach to bathymetric SLAM is validated using multibeam sonar data collected by an autonomous underwater vehicle over a small‐timescale mission (2 h) and a remotely operated vehicle over a large‐timescale mission (11 h). The results demonstrate how observations of the seafloor structure improve the estimated trajectory and resulting map when compared to dead reckoning fused with ultrashort‐baseline or long‐baseline observations. The consistency and robustness of this approach to common errors in navigation is also explored. Furthermore, results are compared with a preexisting state‐of‐the art bathymetric SLAM technique, confirming that similar results can be achieved at a fraction of the computation cost. © 2010 Wiley Periodicals, Inc.

Vision coupled GPS/INS scheme for helicopter navigation

This paper presents a framework for a GPS/INS/vision-based helicopter navigation system. The conventional GPS/INS algorithm has weak points such as GPS blockage and jamming, while the helicopter is a speedy and highly dynamic vehicle that may easily lose a GPS signal. A vision sensor is not affected by signal jamming, and the navigation error of such a system does not accumulate. Hence, a GPS/INS/vision-aided navigation scheme was implemented to provide the robust localization suitable for helicopter operations in various environments. The core algorithm is the vision-based SLAM (simultaneous localization and mapping) technique. Flight tests were performed to verify the SLAM-aided vision navigation algorithm. During the tests, it was confirmed that the developed system is sufficiently robust under GPS blockage conditions. The system design, software algorithm, and flight test results are described in this paper.