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Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (20 September 2020) | Viewed by 53009

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Special Issue Editors

Prof. Dr. Tiancheng Li

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Guest Editor

Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: information fusion; sensor network; positioning; target tracking
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Junkun Yan

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Guest Editor

National Laboratory of Radar Signal Processing, Xidian University, Xian 710071, China.
Interests: Radar signal processing; sensor network; target tracking; MIMO system
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yue Cao

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Guest Editor

School of Transportation Science and Engineering, Beihang University, Beijing 102206, China
Interests: intelligent transport systems; autonomous vehicles; cloud/edge computing, cyber security & localization.

Prof. Dr. Javier Bajo

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Guest Editor

Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Campus Montegancedo, Boadilla del Monte, 28660 Madrid, Spain
Interests: distributed artificial intelligence; knowledge representation; information fusion; mobile sensing; social computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of advanced, arguably intelligent sensors and their massive deployment provide a foundation for new paradigms to combat the challenges that arise in significant tasks such as positioning, tracking, monitoring, and smart sensing in harsh environments with poor prior information. Relevant advances in artificial intelligence (AI) and machine learning (ML) are also finding rapid adoption by industry and fan the fire. Consequently, research on intelligent sensing systems and technologies has attracted considerable attention during the past decade, leading to a variety of effective applications related to intelligent transportation, autonomous vehicles, wearable computing, wireless sensor networks (WSN), and the Internet of Things (IoT). For example, the advent of multiple/massive sensor systems provides a very rich observation at high frequency yet low financial cost, which facilitates novel perspectives based on data clustering, fusion and learning to deal with noises, false alarms, and misdetection, given little statistical knowledge about the objects, sensors, and the environments.

Classic least square methods tangled with neural networks provide another unlimited means to utilize the pattern of data and useful models for positioning, tracking, and monitoring, showing great promise as a means of restoring sensor capability over a range of challenging operating conditions. As such, the sensors community has a great interest in novel, intelligent information fusion, resource optimization, and data mining methods coupling AI and ML for substantial performance enhancement, especially for the challenging scenarios that make traditional approaches inappropriate.

This Special Issue aims to provide broad coverage of recent advances in sensor signal and data mining techniques, algorithms, and approaches, particularly applied for positioning, tracking, monitoring, and smart sensing. Both theoretical and practical works, as well as review/survey papers in the area, are welcome. The topics of interest of this Special Issue include but are not limited to:

Indoor positioning/localization, mobile positioning;
WSN/IoT-based data fusion/learning/mining;
Object recognition/classification using sonar, radar, video, etc.;
Sensor resource optimization;
Intelligent transportation, autonomous vehicles;
Wearable computing, human–machine interaction;
Intelligent sensing systems;
Novel applications of machine learning and artificial intelligence involving the use of sensors.

Prof. Dr. Tiancheng Li
Prof. Dr. Junkun Yan
Prof. Dr. Yue Cao
Prof. Dr. Javier Bajo
Guest Editors

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Keywords

Smart sensing
Positioning
Tracking
Sensor resource management
Wireless sensor network Intelligent transportation
Internet of things
Wearable computing
Cloud/Edge computing
Smart city

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Published Papers (14 papers)

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13 pages, 5435 KiB

Open AccessArticle

A Multi-Core Object Detection Coprocessor for Multi-Scale/Type Classification Applicable to IoT Devices

by Peng Xu, Zhihua Xiao, Xianglong Wang, Lei Chen, Chao Wang and Fengwei An

Sensors 2020, 20(21), 6239; https://doi.org/10.3390/s20216239 - 31 Oct 2020

Cited by 7 | Viewed by 2796

Abstract

Power efficiency is becoming a critical aspect of IoT devices. In this paper, we present a compact object-detection coprocessor with multiple cores for multi-scale/type classification. This coprocessor is capable to process scalable block size for multi-shape detection-window and can be compatible with the frame-image sizes up to 2048 × 2048 for multi-scale classification. A memory-reuse strategy that requires only one dual-port SRAM for storing the feature-vector of one-row blocks is developed to save memory usage. Eventually, a prototype platform is implemented on the Intel DE4 development board with the Stratix IV device. The power consumption of each core in FPGA is only 80.98 mW. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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19 pages, 1845 KiB

Open AccessArticle

An Elaborated Signal Model for Simultaneous Range and Vector Velocity Estimation in FMCW Radar

by Sergei Ivanov, Vladimir Kuptsov, Vladimir Badenko and Alexander Fedotov

Sensors 2020, 20(20), 5860; https://doi.org/10.3390/s20205860 - 16 Oct 2020

Cited by 11 | Viewed by 2850

Abstract

A rigorous mathematical description of the signal reflected from a moving object for radar monitoring tasks using linear frequency modulated continuous wave (LFMCW) microwave radars is proposed. The mathematical model is based on the quasi-relativistic vector transformation of coordinates and Lorentz time. The spatio-temporal structure of the echo signal was obtained taking into account the transverse component of the radar target speed, which made it possible to expand the boundaries of the range of measuring the range and speed of vehicles using LFMCW radars. An algorithm for the simultaneous estimation of the range, radial and transverse components of the velocity vector of an object from the observation data of the time series during one frame of the probing signal is proposed. For an automobile 77 GHz microwave LFMCW radar, a computer experiment was carried out to measure the range and velocity vector of a radar target using the developed mathematical model of the echo signal and an algorithm for estimating the motion parameters. The boundaries of the range for measuring the range and speed of the target are determined. The results of the performed computer experiment are in good agreement with the results of theoretical analysis. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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18 pages, 4365 KiB

Open AccessArticle

Hybrid Solution Combining Kalman Filtering with Takagi–Sugeno Fuzzy Inference System for Online Car-Following Model Calibration

by Mădălin-Dorin Pop, Octavian Proștean, Tudor-Mihai David and Gabriela Proștean

Sensors 2020, 20(19), 5539; https://doi.org/10.3390/s20195539 - 27 Sep 2020

Cited by 9 | Viewed by 3084

Abstract

Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi–Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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20 pages, 10528 KiB

Open AccessArticle

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

by Brahim El Boudani, Loizos Kanaris, Akis Kokkinis, Michalis Kyriacou, Christos Chrysoulas, Stavros Stavrou and Tasos Dagiuklas

Sensors 2020, 20(19), 5495; https://doi.org/10.3390/s20195495 - 25 Sep 2020

Cited by 40 | Viewed by 5204

Abstract

In the near future, the fifth-generation wireless technology is expected to be rolled out, offering low latency, high bandwidth and multiple antennas deployed in a single access point. This ecosystem will help further enhance various location-based scenarios such as assets tracking in smart factories, precise smart management of hydroponic indoor vertical farms and indoor way-finding in smart hospitals. Such a system will also integrate existing technologies like the Internet of Things (IoT), WiFi and other network infrastructures. In this respect, 5G precise indoor localization using heterogeneous IoT technologies (Zigbee, Raspberry Pi, Arduino, BLE, etc.) is a challenging research area. In this work, an experimental 5G testbed has been designed integrating C-RAN and IoT networks. This testbed is used to improve both vertical and horizontal localization (3D Localization) in a 5G IoT environment. To achieve this, we propose the DEep Learning-based co-operaTive Architecture (DELTA) machine learning model implemented on a 3D multi-layered fingerprint radiomap. The DELTA begins by estimating the 2D location. Then, the output is recursively used to predict the 3D location of a mobile station. This approach is going to benefit use cases such as 3D indoor navigation in multi-floor smart factories or in large complex buildings. Finally, we have observed that the proposed model has outperformed traditional algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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15 pages, 1132 KiB

Open AccessArticle

Extended Target Marginal Distribution Poisson Multi-Bernoulli Mixture Filter

by Haocui Du and Weixin Xie

Sensors 2020, 20(18), 5387; https://doi.org/10.3390/s20185387 - 20 Sep 2020

Cited by 3 | Viewed by 2568

Abstract

The existence of clutter, unknown measurement sources, unknown number of targets, and undetected probability are problems for multi-extended target tracking, to address these problems; this paper proposes a gamma-Gaussian-inverse Wishart (GGIW) implementation of a marginal distribution Poisson multi-Bernoulli mixture (MD-PMBM) filter. Unlike existing multiple extended target tracking filters, the GGIW-MD-PMBM filter computes the marginal distribution (MD) and the existence probability of each target, which can shorten the computing time while maintaining good tracking results. The simulation results confirm the validity and reliability of the GGIW-MD-PMBM filter. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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28 pages, 6822 KiB

Open AccessArticle

Autonomous Road Roundabout Detection and Navigation System for Smart Vehicles and Cities Using Laser Simulator–Fuzzy Logic Algorithms and Sensor Fusion

by Mohammed A. H. Ali, Musa Mailah, Waheb A. Jabbar, Khaja Moiduddin, Wadea Ameen and Hisham Alkhalefah

Sensors 2020, 20(13), 3694; https://doi.org/10.3390/s20133694 - 1 Jul 2020

Cited by 16 | Viewed by 4983

Abstract

A real-time roundabout detection and navigation system for smart vehicles and cities using laser simulator–fuzzy logic algorithms and sensor fusion in a road environment is presented in this paper. A wheeled mobile robot (WMR) is supposed to navigate autonomously on the road in real-time and reach a predefined goal while discovering and detecting the road roundabout. A complete modeling and path planning of the road’s roundabout intersection was derived to enable the WMR to navigate autonomously in indoor and outdoor terrains. A new algorithm, called Laser Simulator, has been introduced to detect various entities in a road roundabout setting, which is later integrated with fuzzy logic algorithm for making the right decision about the existence of the roundabout. The sensor fusion process involving the use of a Wi-Fi camera, laser range finder, and odometry was implemented to generate the robot’s path planning and localization within the road environment. The local maps were built using the extracted data from the camera and laser range finder to estimate the road parameters such as road width, side curbs, and roundabout center, all in two-dimensional space. The path generation algorithm was fully derived within the local maps and tested with a WMR platform in real-time. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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24 pages, 8006 KiB

Open AccessArticle

Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

by Wei Zhou, Wei Wang and De Zhao

Sensors 2020, 20(12), 3555; https://doi.org/10.3390/s20123555 - 23 Jun 2020

Cited by 22 | Viewed by 4195

Abstract

The metro system plays an important role in urban public transit, and the passenger flow forecasting is fundamental to assisting operators establishing an intelligent transport system (ITS). The forecasting results can provide necessary information for travelling decision of travelers and metro operations of managers. In order to investigate the inner characteristics of passenger flow and make a more accurate prediction with less training time, a novel model (i.e., SSA-AWELM), a combination of singular spectrum analysis (SSA) and AdaBoost-weighted extreme learning machine (AWELM), is proposed in this paper. SSA is developed to decompose the original data into three components of trend, periodicity, and residue. AWELM is developed to forecast each component desperately. The three predicted results are summed as the final outcomes. In the experiments, the dataset is collected from the automatic fare collection (AFC) system of Hangzhou metro in China. We extracted three weeks of passenger flow to carry out multistep prediction tests and a comparison analysis. The results indicate that the proposed SSA-AWELM model can reduce both predicted errors and training time. In particular, compared with the prevalent deep-learning model long short-term memory (LSTM) neural network, SSA-AWELM has reduced the testing errors by 22% and saved time by 84%, on average. It demonstrates that SSA-AWELM is a promising approach for passenger flow forecasting. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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18 pages, 6416 KiB

Open AccessArticle

MIMU/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage: Testing and Results

by Kai Zhu, Xuan Guo, Changhui Jiang, Yujingyang Xue, Yuanjun Li, Lin Han and Yuwei Chen

Sensors 2020, 20(8), 2302; https://doi.org/10.3390/s20082302 - 17 Apr 2020

Cited by 15 | Viewed by 3997

Abstract

With the rapid development of autonomous vehicles, the demand for reliable positioning results is urgent. Currently, the ground vehicles heavily depend on the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) providing reliable and continuous navigation solutions. In dense urban areas, especially narrow streets with tall buildings, the GNSS signals are possibly blocked by the surrounding tall buildings, and under this condition, the geometry distribution of the in-view satellites is very poor, and the None-Line-Of-Sight (NLOS) and Multipath (MP) heavily affects the positioning accuracy. Further, the INS positioning errors will quickly diverge over time without the GNSS correction. Aiming at improving the position accuracy under signal challenging environment, in this paper, we developed an MIMU(Micro Inertial Measurement Unit)/Odometer integration system with vehicle state constraints (MO-C) for improving the vehicle positioning accuracy without GNSS. MIMU/Odometer integration model and the constrained measurements are given in detail. Several field tests were carried out for evaluating and assessing the MO-C system. The experiments were divided into two parts, firstly, field testing with data post-processing and real-time processing was carried out for fully assessing the performance of the MO-C system. Secondly, the MO-C was implemented in the BeiDou Satellite Navigation System (BDS)/integrated navigation system (INS) for evaluating the MO-C performance during the BDS signal outage. The MIMU standalone positioning accuracy was compared with that from the MIMU/Odometer integration (MO), MO-C and MIMU with constraints (M-C) for assessing the Odometer, and the influence of the constraint on the positioning errors reduction. The results showed that the latitude and longitude errors could be suppressed with Odometer assisting, and the height errors were suppressed while the state constraints were included. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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17 pages, 1680 KiB

Open AccessArticle

A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

by Zhihang Yue, Sen Zhang and Wendong Xiao

Sensors 2020, 20(7), 2147; https://doi.org/10.3390/s20072147 - 10 Apr 2020

Cited by 41 | Viewed by 5004

Abstract

Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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14 pages, 6457 KiB

Open AccessArticle

A Robust Multi-Sensor Data Fusion Clustering Algorithm Based on Density Peaks

by Jiande Fan, Weixin Xie and Haocui Du

Sensors 2020, 20(1), 238; https://doi.org/10.3390/s20010238 - 31 Dec 2019

Cited by 12 | Viewed by 3578

Abstract

In this paper, a novel multi-sensor clustering algorithm, based on the density peaks clustering (DPC) algorithm, is proposed to address the multi-sensor data fusion (MSDF) problem. The MSDF problem is raised in the multi-sensor target detection (MSTD) context and corresponds to clustering observations of multiple sensors, without prior information on clutter. During the clustering process, the data points from the same sensor cannot be grouped into the same cluster, which is called the cannot link (CL) constraint; the size of each cluster should be within a certain range; and overlapping clusters (if any) must be divided into multiple clusters to satisfy the CL constraint. The simulation results confirm the validity and reliability of the proposed algorithm. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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13 pages, 2020 KiB

Open AccessLetter

A Unified Fourth-Order Tensor-Based Smart Community System

by Chang Liu, Huaiyu Wu, Junyuan Wang and Mingkai Wang

Sensors 2020, 20(21), 5990; https://doi.org/10.3390/s20215990 - 22 Oct 2020

Cited by 1 | Viewed by 2468

Abstract

Empowered by the ubiquitous sensing capabilities of Internet of Things (IoT) technologies, smart communities could benefit our daily life in many aspects. Various smart community studies and practices have been conducted, especially in China thanks to the government’s support. However, most intelligent systems are designed and built individually by different manufacturers in diverging platforms with different functionalities. Therefore, multiple individual systems must be deployed in a smart community to have a set of functions, which could lead to hardware waste, high energy consumption and high deployment cost. More importantly, current smart community systems mainly focus on the technologies involved, while the effects of human activity are neglected. In this paper, a fourth-order tensor model representing object, time, location and human activity is proposed for human-centered smart communities, based on which a unified smart community system is designed. Thanks to the powerful data management abilities of a high-order tensor, multiple functions can be integrated into our system. In addition, since the tensor model embeds human activity information, complex functions could be implemented by exploring the effects of human activity. Two exemplary applications are presented to demonstrate the flexibility of the proposed unified fourth-order tensor-based smart community system. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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15 pages, 1746 KiB

Open AccessLetter

LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion

by Nico Podevijn, Jens Trogh, Michiel Aernouts, Rafael Berkvens, Luc Martens, Maarten Weyn, Wout Joseph and David Plets

Sensors 2020, 20(20), 5815; https://doi.org/10.3390/s20205815 - 14 Oct 2020

Cited by 8 | Viewed by 5011

Abstract

In contrast to accurate GPS-based localization, approaches to localize within LoRaWAN networks offer the advantages of being low power and low cost. This targets a very different set of use cases and applications on the market where accuracy is not the main considered metric. The localization is performed by the Time Difference of Arrival (TDoA) method and provides discrete position estimates on a map. An accurate “tracking-on-demand” mode for retrieving lost and stolen assets is important. To enable this mode, we propose deploying an e-compass in the mobile LoRa node, which frequently communicates directional information via the payload of the LoRaWAN uplink messages. Fusing this additional information with raw TDoA estimates in a map matching algorithm enables us to estimate the node location with a much increased accuracy. It is shown that this sensor fusion technique outperforms raw TDoA at the cost of only embedding a low-cost e-compass. For driving, cycling, and walking trajectories, we obtained minimal improvements of 65, 76, and 82% on the median errors which were reduced from 206 to 68 m, 197 to 47 m, and 175 to 31 m, respectively. The energy impact of adding an e-compass is limited: energy consumption increases by only 10% compared to traditional LoRa localization, resulting in a solution that is still 14 times more energy-efficient than a GPS-over-LoRa solution. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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17 pages, 11956 KiB

Open AccessLetter

Leveraging Uncertainties in Softmax Decision-Making Models for Low-Power IoT Devices

by Chiwoo Cho, Wooyeol Choi and Taewoon Kim

Sensors 2020, 20(16), 4603; https://doi.org/10.3390/s20164603 - 16 Aug 2020

Cited by 10 | Viewed by 2917

Abstract

Internet of Things (IoT) devices bring us rich sensor data, such as images capturing the environment. One prominent approach to understanding and utilizing such data is image classification which can be effectively solved by deep learning (DL). Combined with cross-entropy loss, softmax has been widely used for classification problems, despite its limitations. Many efforts have been made to enhance the performance of softmax decision-making models. However, they require complex computations and/or re-training the model, which is computationally prohibited on low-power IoT devices. In this paper, we propose a light-weight framework to enhance the performance of softmax decision-making models for DL. The proposed framework operates with a pre-trained DL model using softmax, without requiring any modification to the model. First, it computes the level of uncertainty as to the model’s prediction, with which misclassified samples are detected. Then, it makes a probabilistic control decision to enhance the decision performance of the given model. We validated the proposed framework by conducting an experiment for IoT car control. The proposed model successfully reduced the control decision errors by up to 96.77% compared to the given DL model, and that suggests the feasibility of building DL-based IoT applications with high accuracy and low complexity. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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16 pages, 1152 KiB

Open AccessLetter

Computationally Efficient Cooperative Dynamic Range-Only SLAM Based on Sum of Gaussian Filter

by Jung-Hee Kim and Doik Kim

Sensors 2020, 20(11), 3306; https://doi.org/10.3390/s20113306 - 10 Jun 2020

Cited by 4 | Viewed by 2962

Abstract

A cooperative dynamic range-only simultaneous localization and mapping (CDRO-SLAM) algorithm based on the sum of Gaussian (SoG) filter was recently introduced. The main characteristics of the CDRO-SLAM are (i) the integration of inter-node ranges as well as usual direct robot-node ranges to improve the convergence rate and localization accuracy and (ii) the tracking of any moving nodes under dynamic environments by resetting and updating the SoG variables. In this paper, an efficient implementation of the CDRO-SLAM (eCDRO-SLAM) is proposed to mitigate the high computational burden of the CDRO-SLAM due to the inter-node measurements. Furthermore, a thorough computational analysis is presented, which reveals that the computational efficiency of the eCDRO-SLAM is significantly improved over the CDRO-SLAM. The performance of the proposed eCDRO-SLAM is compared with those of several conventional RO-SLAM algorithms and the results show that the proposed efficient algorithm has a faster convergence rate and a similar map estimation error regardless of the map size. Accordingly, the proposed eCDRO-SLAM can be utilized in various RO-SLAM applications. Full article

(This article belongs to the Special Issue Intelligent Sensors for Positioning, Tracking, Monitoring, Navigation and Smart Sensing in Smart Cities)

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