The front-end feature matching module of traditional slam systems is characterized by sparse or dense feature points, it is difficult to generate accurate camera trajectory and scene reconstruction results, in respons...
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The front-end feature matching module of traditional slam systems is characterized by sparse or dense feature points, it is difficult to generate accurate camera trajectory and scene reconstruction results, in response to this problem, the author studied a fast reconstruction algorithm for any path based on V-slam, by using improved feature matching algorithms to accurately match feature points, the accuracy of scene sparse reconstruction and camera trajectory recovery has been improved, the backend optimization thread adopts segmented optimization matching to reduce the computational burden of reconstruction, and the performance of the V-slam system was improved through parallel processing, the matching results and camera trajectory error comparison results showed that the improved V-slam algorithm can quickly recover camera trajectory and scene reconstruction, with the development of multi-sensor collaborative coupling and multi view fusion technology, the V-slam method proposed by the author can add virtual 3D objects to real scenes, and the Vslam system can extract feature points in the screen in real-time and detect planar objects in the scene, ensure that multiple virtual objects in the scene meet geometric consistency with the actual scene, in the experiment, two objects were added to the virtual scene, users can interactively scale objects and add them without being affected by camera movements, ensuring consistency between objects and the real scene.
When the multi-mode electronic sensor based on information fusion works, it will be interfered by various noises in the process of data acquisition and transmission, and the signal will appear nonlinear distortion. Th...
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slam technology is one of the important technologies in modern robot, automatic driving and other fields, which can carry out autonomous positioning and map building in unknown environment. In indoor motion measuremen...
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The purpose of the project is to design and test of a slam algorithm and then assembling the hardware's on a Pi- Car (prototype robot) which can move and travel through different place and generate a 2D map out of...
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The purpose of the project is to design and test of a slam algorithm and then assembling the hardware's on a Pi- Car (prototype robot) which can move and travel through different place and generate a 2D map out of the places. With the current competition in the market as most of the fortune 500 companies are trying to use this technology (slam algorithm) on different platforms like autonomous Car (Google Car, Tesla Car) etc., we thought it was a good idea if we could built a prototype for the same. The slam algorithm implemented on the robot works on the principle of Localization, mapping and navigate itself in the room (for out project). The System uses mainly Raspberry-Pi model B which integrates with the Lidar and Ultrasonic sensor. The Lidar sensor does laser scanning and gives accurate result back to the raspberry-pi, likewise the ultrasonic sensor gives accurate data (for close range), which detects any obstacle in front of it and sends the signal to the raspberry-pi to make a turn for the Pi- Car.
slam, or simultaneous localization and mapping, is primarily used to solve the problem of mobile robot positioning, navigation, and mapping in unknown environments. Among these, 2D laser slam has low cost and high acc...
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slam, or simultaneous localization and mapping, is primarily used to solve the problem of mobile robot positioning, navigation, and mapping in unknown environments. Among these, 2D laser slam has low cost and high accuracy in building drawings in an indoor environment. This paper describes the fundamental framework of 2D laser slam. Several popular slam algorithms are discussed. Finally, future developments in laser slam are anticipated.
Currently, China has entered the aging society, and the problems of elderly care have been focused by the whole society. In order to help the young people to take care of the elderly, a care service system integrated ...
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ISBN:
(纸本)9783030169466;9783030169459
Currently, China has entered the aging society, and the problems of elderly care have been focused by the whole society. In order to help the young people to take care of the elderly, a care service system integrated with slam algorithm was designed. This system includes four parts: ZigBee networking equipments, robot, cloud server and Andriod APP. It makes the robot based on slam algorithm achieve indoor navigation and map building. And it provides convenience for the disabled people, monitors the environment security information in real time and shows the physical data of the elderly, which can help young people who work outside master the physical healthy conditions of the elderly who are at home.
In order to realize the positioning and creation of the environment of mobile robots, this article proposes an optimized coverage robot slam algorithm based on an improved particle filter for WSN nodes. The algorithm ...
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In order to realize the positioning and creation of the environment of mobile robots, this article proposes an optimized coverage robot slam algorithm based on an improved particle filter for WSN nodes. The algorithm overcomes the disadvantages of the standard particle filter slam algorithm in the simultaneous positioning of robot poses and creation of environmental maps. By constructing the sensor node to cover the high coverage of the slam positioning information node of the robot, the algorithm can search for the ideal result under the existing information, and the local optimization is performed to obtain the ideal result in another local state. Thus, the global accurate robot slam information is finally obtained. Simulation experiments show that the influence of the time delay parameter for simultaneous positioning of the robot slam is almost zero at different speeds, which shows the superior positioning stability of the new algorithm.
Small-scale Unmanned Aerial Vehicles (UAVs) have recently been used in several application areas, including search and rescue operations, precision agriculture, and environmental monitoring. Telemetry data, acquired b...
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ISBN:
(纸本)9783030306458;9783030306441
Small-scale Unmanned Aerial Vehicles (UAVs) have recently been used in several application areas, including search and rescue operations, precision agriculture, and environmental monitoring. Telemetry data, acquired by GPSs, plays a key role in supporting activities in areas like those just reported. In particular, this data is often used for the real-time computation of UAVs paths and heights, which are basic prerequisites for many tasks. In some cases, however, the GPS sensors can lose their satellite connection, thus making the telemetry data acquisition impossible. This paper presents a feature-based Simultaneous Localisation and Mapping (slam) algorithm for small-scale UAVs with nadir view. The proposed algorithm allows to know the travelled route as well as the flight height by using both a calibration step and visual features extracted from the acquired images. Due to the novelty of the proposed algorithm no comparisons with other methods are reported. Anyway, extensive experiments on the recently released UAV Mosaicking and Change Detection (UMCD) dataset have shown the effectiveness and robustness of the proposed algorithm. The latter and the dataset can be used as baseline for future research in this application area.
For the problem of the error of EKF in linearization, an iterated extended Kalman filtering algorithm based on IMU and laser sensor for environmental feature matching is proposed. The K-Nearest Neighbor algorithm is a...
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ISBN:
(纸本)9781450353656
For the problem of the error of EKF in linearization, an iterated extended Kalman filtering algorithm based on IMU and laser sensor for environmental feature matching is proposed. The K-Nearest Neighbor algorithm is applied to match the data obtained by IMU and laser sensor. In the measurement phase, the nonlinear system is linearized multiple by EKF. The simulation results show that the new method effectively reduces the nonlinear error and inhibits the error accumulation caused by IMU. Compared with traditional extended Kalman filtering, the root mean square error of position and azimuth was reduced by 48%, 16% and 29%, respectively. As the result, the navigation accuracy of system is improved and state tracking performance outperforms better than traditional EKF.
For simultaneous localization and mapping (slam) of mobile robots, an innovative solution is proposed, named adaptive square root cubature Kalman filter based slam algorithm (ASRCKF-slam). The main contribution of the...
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ISBN:
(纸本)9781479970988
For simultaneous localization and mapping (slam) of mobile robots, an innovative solution is proposed, named adaptive square root cubature Kalman filter based slam algorithm (ASRCKF-slam). The main contribution of the proposed algorithm lies that: 1) Square root factors are used in the proposed ASRCKF-slam algorithm to improve the calculation efficiency by avoiding the time-consuming Cholesky decompositions. 2) Using the adaptive Sage-Husa estimator to solve the large estimation errors or even divergence problem caused by the time-varying or unknown noise. Simulation results obtained demonstrate that the proposed ASRCKF-slam algorithm is superior to the existed slam method in the aspect of estimation accuracy and computational efficiency.
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