A Novel RGB-D SLAM Algorithm Based on Cloud Robotics

Li, Yanli; Zhang, Heng; Huang, Chao

doi:10.3390/s19235288

Cited by 14 publications

(6 citation statements)

References 50 publications

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“…The platform plus manipulator would be able to move to the selected NBV, avoiding obstacles in the way. In this scenario, a fleet of vehicles could all contribute to the same exploration, requiring collaborative behaviors for coordination and information sharing between them [39] or even to offload the most computationally intensive tasks to an external infrastructure (cloud robotics) [40]. Lastly, possible combining the RGB data, the robot could detect a new object placed before it, to then classify it [41].…”

Section: Discussionmentioning

confidence: 99%

Autonomous Scene Exploration for Robotics: A Conditional Random View-Sampling and Evaluation Using a Voxel-Sorting Mechanism for Efficient Ray Casting

Santos

Oliveira

Arrais

et al. 2020

Sensors

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Carrying out the task of the exploration of a scene by an autonomous robot entails a set of complex skills, such as the ability to create and update a representation of the scene, the knowledge of the regions of the scene which are yet unexplored, the ability to estimate the most efficient point of view from the perspective of an explorer agent and, finally, the ability to physically move the system to the selected Next Best View (NBV). This paper proposes an autonomous exploration system that makes use of a dual OcTree representation to encode the regions in the scene which are occupied, free, and unknown. The NBV is estimated through a discrete approach that samples and evaluates a set of view hypotheses that are created by a conditioned random process which ensures that the views have some chance of adding novel information to the scene. The algorithm uses ray-casting defined according to the characteristics of the RGB-D sensor, and a mechanism that sorts the voxels to be tested in a way that considerably speeds up the assessment. The sampled view that is estimated to provide the largest amount of novel information is selected, and the system moves to that location, where a new exploration step begins. The exploration session is terminated when there are no more unknown regions in the scene or when those that exist cannot be observed by the system. The experimental setup consisted of a robotic manipulator with an RGB-D sensor assembled on its end-effector, all managed by a Robot Operating System (ROS) based architecture. The manipulator provides movement, while the sensor collects information about the scene. Experimental results span over three test scenarios designed to evaluate the performance of the proposed system. In particular, the exploration performance of the proposed system is compared against that of human subjects. Results show that the proposed approach is able to carry out the exploration of a scene, even when it starts from scratch, building up knowledge as the exploration progresses. Furthermore, in these experiments, the system was able to complete the exploration of the scene in less time when compared to human subjects.

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Section: Discussionmentioning

confidence: 99%

Autonomous Scene Exploration for Robotics: A Conditional Random View-Sampling and Evaluation Using a Voxel-Sorting Mechanism for Efficient Ray Casting

Santos

Oliveira

Arrais

et al. 2020

Sensors

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show abstract

“…ese positioning methods use different sensors and cooperate with the robot to move dynamically, so that the robot can realize high-precision positioning and navigation under the restriction of different environments. Literature [9,10] puts forward the square-root unscented KF (Kalman filter) based on UKF, which expands the restricted range of UKF and can be applied to Gaussian regression process. Literature [11] puts forward a new idea; that is, traditional UKF is combined with a new hotspot algorithm ParticleFilter, thus forming a high-precision unscented particle filter algorithm.…”

Section: Related Workmentioning

confidence: 99%

Research on Robot Positioning and Navigation Algorithm Based on SLAM

Yating

2022

Wireless Communications and Mobile Computing

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In the industrial field, industrial robots have taken over the heavy lifting that used to be done by traditional handicraft assembly lines, greatly freeing up human resources and improving production efficiency and safety. As a result, the focus of this paper is on the SLAM-based robot localization and navigation algorithm (simultaneous localization and mapping). An attitude estimation algorithm based on KF (Kalman filtering) information fusion of vision SLAM and IMU (Inertial Measurement Unit) is proposed, and the ORB-SLAM algorithm is studied and perfected. The fusion of the two postures improves the accuracy and frequency of the robot’s attitude estimation during motion. In addition, PSO (Particle Swarm Optimization) technology is used to optimize the resampling process, and PSO optimizes the particle set to alleviate the problem of particle degradation and exhaustion caused by resampling in the FastSLAM algorithm. Finally, the algorithm is verified to meet the requirements of positioning and composition accuracy, as well as the feasibility and effectiveness of robot autonomous navigation, using the open simulation platform.

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“…Kinect sensor belongs to a special class of vision-based sensors, where the sensor has the ability to produce a 3D map. These sensors perform with better accuracy as compared to the LASER sensor; however, it has a limited field of view which renders applications limited in many practical ways [115,116].…”

Section: Kinectmentioning

confidence: 99%

Investigation of Widely Used SLAM Sensors Using Analytical Hierarchy Process

et al. 2022

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Applications of mobile robots are continuously capturing the importance in numerous areas such as agriculture, surveillance, defense, and planetary exploration to name a few. Accurate navigation of a mobile robot is highly significant for its uninterrupted operation. Simultaneous localization and mapping (SLAM) is one of the widely used techniques in mobile robots for localization and navigation. SLAM consists of front- and back-end processes, wherein the front-end includes SLAM sensors. These sensors play a significant role in acquiring accurate environmental information for further processing and mapping. Therefore, understanding the operational limits of the available SLAM sensors and data collection techniques from a single sensor or multisensors is noteworthy. In this article, a detailed literature review of widely used SLAM sensors such as acoustic sensor, RADAR, camera, Light Detection and Ranging (LiDAR), and RGB-D is provided. The performance of SLAM sensors is compared using an analytical hierarchy process (AHP) based on various key indicators such as accuracy, range, cost, working environment, and computational cost.

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A Novel RGB-D SLAM Algorithm Based on Cloud Robotics

Cited by 14 publications

References 50 publications

Autonomous Scene Exploration for Robotics: A Conditional Random View-Sampling and Evaluation Using a Voxel-Sorting Mechanism for Efficient Ray Casting

Autonomous Scene Exploration for Robotics: A Conditional Random View-Sampling and Evaluation Using a Voxel-Sorting Mechanism for Efficient Ray Casting

Research on Robot Positioning and Navigation Algorithm Based on SLAM

Investigation of Widely Used SLAM Sensors Using Analytical Hierarchy Process

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