Review of SLAM Algorithms for Indoor Mobile Robot with LIDAR and RGB-D Camera Technology

Kolhatkar, Chinmay; Wagle, Kranti

doi:10.1007/978-981-15-4692-1_30

Cited by 36 publications

(18 citation statements)

References 11 publications

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“…(3) Prior Knowledge Correction. Most SLAM algorithms assume that the control noise statistics Q and observation x t [1] ,𝜔 t (𝜇 t [1,1] ,∑ t [1,1] )…”

Section: Parallel Computingmentioning

confidence: 99%

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A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

Zhang

Chen³

2022

Journal of Sensors

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Simultaneous localization and mapping (SLAM) is a typical computing-intensive task. Based on its own computing power, a mobile robot has difficult meeting the real-time performance and accuracy requirements for the SLAM process at the same time. Benefiting from the rapid growth of the network data transmission rate, cloud computing technology begins to be applied in the robotics. There is the reliability problem caused by solely relying on cloud computing. To compensate for the insufficient airborne capacity, ensure the real-time performance and reliability, and improve the accuracy, a SLAM algorithm based on edge-cloud collaborative computing is proposed. The edge estimates the mobile robot pose and the local map using a square root unscented Kalman filter (SR-UKF). The cloud estimates the mobile robot pose and the global map using a distributed square root unscented particle filter (DSR-UPF). By using sufficient particles in the cloud, DSR-UPF can improve the SLAM accuracy. The cloud returns the particle with the largest posteriori probability to the edge, and the edge performs edge-cloud data fusion based on probability. Both the simulation and the experimental results show that the proposed algorithm can improve the estimation accuracy and reduce the execution time at the same time. By transferring the heavy computation from robots to the cloud, it can enhance the environmental adaptability of mobile robots.

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“…(3) Prior Knowledge Correction. Most SLAM algorithms assume that the control noise statistics Q and observation x t [1] ,𝜔 t (𝜇 t [1,1] ,∑ t [1,1] )…”

Section: Parallel Computingmentioning

confidence: 99%

“…Since the beginning of the 21st century, autonomous navigation technology has received increasing attention [1,2]. It is among the disruptive technologies predicted by academic institutions and groups, such as the McKinsey Global Institute, Citi GPS MIT, and the ARK investment management company [3,4].…”

Section: Introductionmentioning

confidence: 99%

A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

Zhang

Chen³

2022

Journal of Sensors

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“…Singh and Khelchandra [39] used MLP to determine a collision-free area that controls the robot speed in each motion. A review of SLAM algorithms for Robot Navigation was addressed in [20]. -Robot path planning Dirik et al [9] proposed a global path planning method based on ANNs and GAs to provide an effective path planning and obstacle avoidance solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Guided Autonomous Nursing Robot through Wireless Beacon Network

Narayanan¹,

Krishnan

Sơn

et al. 2021

Multimed Tools Appl

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Robotics is one of the most emerging technologies today, and are used in a variety of applications, ranging from complex rocket technology to monitoring of crops in agriculture. Robots can be exceptionally useful in a smart hospital environment provided that they are equipped with improved vision capabilities for detection and avoidance of obstacles present in their path, thus allowing robots to perform their tasks without any disturbance. In the particular case of Autonomous Nursing Robots, major essential issues are effective robot path planning for the delivery of medicines to patients, measuring the patient body parameters through sensors, interacting with and informing the patient, by means of voicebased modules, about the doctors visiting schedule, his/her body parameter details, etc. This paper presents an approach of a complete Autonomous Nursing Robot which supports all the aforementioned tasks. In this paper, we present a new Autonomous Nursing Robot system capable of operating in a smart hospital environment area. The objective of the system is to identify the patient room, perform robot path planning for the delivery of medicines to a patient, and measure the patient body parameters, through a wireless BLE (Bluetooth Low Energy) beacon receiver and the BLE beacon transmitter at the respective patient rooms. Assuming that a wireless beacon is kept at the patient room, the robot follows the beacon's signal, identifies the respective room and delivers the needed medicine to the patient. A new fuzzy controller system which consists of three ultrasonic sensors and one camera is developed to detect the optimal robot path and to avoid the robot collision with stable and moving obstacles. The fuzzy controller effectively detects obstacles in the robot's vicinity and makes proper decisions for avoiding them. The navigation of the robot is implemented on a BLE tag module by using the AOA (Angle of Arrival) method. The robot uses sensors to measure the patient body parameters and updates these data to the hospital patient database system in a private cloud mode. It also makes uses of a Google assistant to interact with the patients. The robotic system was implemented on the Raspberry Pi using Matlab 2018b. The system performance was evaluated on a PC with an Intel Core i5 processor, while the solar power was used to power the system. Several sensors, namely HC-SR04 ultrasonic sensor, Logitech HD 720p image sensor, a temperature sensor and a heart rate sensor are used together with a camera to generate datasets for testing the proposed system. In particular, the system was tested on operations taking place in the context of a private hospital in Tirunelveli, Tamilnadu, India. A detailed comparison is performed, through some performance metrics, such as Correlation, Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), against the related works of Deepu et al., Huh and Seo, Chinmayi et al., Alli et al., Xu, Ran et al., and Lee et al. The experimental system validation showed that the fuzzy co...

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“…Mobile robots typically receive sensor information from their attached sensors [ 1 ], for example, in the form of 2D projections of image frames or 3D spatial points from high-frequency LiDAR scans [ 2 ]. However, this perceived information is often insufficient for the robot to navigate as it lacks the geometric understanding and reconstruction of the scene.…”

Section: Introductionmentioning

confidence: 99%

A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-Based Semantic Scene Understanding Using Multi-Modal Sensor Fusion

Lai

2022

Sensors

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Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previously unseen environment while simultaneously localising itself with respect to the map. In particular, Visual-SLAM uses various sensors from the mobile robot for collecting and sensing a representation of the map. Traditionally, geometric model-based techniques were used to tackle the SLAM problem, which tends to be error-prone under challenging environments. Recent advancements in computer vision, such as deep learning techniques, have provided a data-driven approach to tackle the Visual-SLAM problem. This review summarises recent advancements in the Visual-SLAM domain using various learning-based methods. We begin by providing a concise overview of the geometric model-based approaches, followed by technical reviews on the current paradigms in SLAM. Then, we present the various learning-based approaches to collecting sensory inputs from mobile robots and performing scene understanding. The current paradigms in deep-learning-based semantic understanding are discussed and placed under the context of Visual-SLAM. Finally, we discuss challenges and further opportunities in the direction of learning-based approaches in Visual-SLAM.

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Review of SLAM Algorithms for Indoor Mobile Robot with LIDAR and RGB-D Camera Technology

Cited by 36 publications

References 11 publications

A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

Fuzzy Guided Autonomous Nursing Robot through Wireless Beacon Network

A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-Based Semantic Scene Understanding Using Multi-Modal Sensor Fusion

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