In this paper, we propose a new image denoising method based on Gaussian filter and Non-local means filter. The new algorithm dealing with the image noise learns from the weighted average thoughts of particle filter. we analyse the difference between the proposed method and other two algorithms, then we do multi-group experiments to compare the denoising effect of these methods, and evaluate their performance mainly through visual effect and peak signal-to-noise ratio (PSNR). The experimental results show that the proposed new method is effective in image denoising.

A new image denoising method based on Gaussian filter

To address the issue of low positioning accuracy of mobile robots in trellis kiwifruit orchards with weak signal environments, this study investigated an outdoor integrated positioning method based on ultra-wideband (UWB), light detection and ranging (LiDAR), and odometry (ODOM). Firstly, a dynamic error correction strategy using the Kalman filter (KF) was proposed to enhance the dynamic positioning accuracy of UWB. Secondly, the particle filter algorithm (PF) was employed to fuse UWB/ODOM/LiDAR measurements, resulting in an extended Kalman filter (EKF) measurement value. Meanwhile, the odometry value served as the predicted value in the EKF. Finally, the predicted and measured values were fused through the EKF to estimate the robot's pose. Simulation results demonstrated that the UWB/ODOM/LiDAR integrated positioning method achieved a mean lateral error of 0.076 m and a root mean square error (RMSE) of 0.098 m. Field tests revealed that compared to standalone UWB positioning, UWB-based KF positioning, and LiDAR/ODOM integrated positioning methods, the proposed approach improved the positioning accuracy by 64.8%, 13.8%, and 38.3%, respectively. Therefore, the proposed integrated positioning method exhibits promising positioning performance in trellis kiwifruit orchards with potential applicability to other orchard environments.

Integrated Positioning System of Kiwifruit Orchard Mobile Robot Based on UWB/LiDAR/ODOM.

In the robot development, different robot developers have to realize their specific software architecture and hardware implementation, which leads to the complexity of the system that different developers design is not the same, different architecture can make a big difference If developers want has other functions on the basis of the original robot, it is difficult to achieve rapid transfer of software, and in the design of the system, need to do more cumbersome, repetitive work module etc This makes the development cycle may be very long ROS provides a similar combination of Observer mode and Mediator mode communication architecture, which can meet most scene of the robots use, and ROS has a fairly large node code base, the use of ROS can greatly reduce the workload of the robot system design, reduce duplication developers, improve code reuse rate In this article, the paper introduces the algorithm of particle filter, and then puts forward a design method of indoor intelligent patrol robot based on ROS communication architecture By using the navigation function package of ROS, the autonomous navigation of indoor intelligent patrol robot can be realized, and the navigation process can be carried out with escaping obstacles in time, intelligence is very obvious in the progress Experiments show that this method is fast and efficient, and it is a very efficient indoor intelligent patrol robot design method

Indoor intelligent patrol robot based on ROS architecture

In order to reduce the influence of the initial value on the filtering result, a fusion filter is proposed in this paper, and it is applied to the AGV localization. The observation noise of the laser is assumed to be subjected to Gaussian distribution. The initial state of the robot is firstly obtained by the particle filter, and then be used by the extended Kalman filter for localization. The experiment result shows that the fusion filter algorithm eliminates the error caused by the initial error compared with the simple extended Kalman filter algorithm. No significant cumulative error occurred, and the accuracy increased by 10 times. In the complex indoor environment, it can be a kind of effective positioning method.

Application of fusion filtering algorithm in AGV localization

Sequential particle filter is a kind method of nonlinear filtering algorithm based on the Monte Carlo simulation,theories and realization steps of general particle filter algorithm are introduced in detail,and simulations are carried.The results show that particle filter is a good simulation of nonlinear systems,the estimated accuracy is superior to extended Kalman filter.The particle filter algorithm is to solve nonlinear filtering issues in which random variables must meet the conditionality of the Gaussian distribution,to some extent the degeneracy problem is solved.So the algorithm is successfully applied in many areas recently.

Research on particle filter algorithm and its applications

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Research on particle filter algorithm and its applications