Aiming at the problem of low sampling efficiency and high demand for anchor node density of traditional Monte Carlo Localization Boxed algorithm, an improved algorithm based on historical anchor node information and the received signal strength indicator (RSSI) ranging weight is proposed which can effectively constrain sampling area of the node to be located. Moreover, the RSSI ranging of the surrounding anchors and the neighbor nodes is used to provide references for the position sampling weights of the nodes to be located, an improved motion model is proposed to further restrict the sampling area in direction. The simulation results show that the improved Monte Carlo Localization Boxed (IMCB) algorithm effectively improves the accuracy and efficiency of localization.

/pdf/an-improved-mcb-localization-algorithm-based-on-weighted-575ql3l5q6.pdf

An improved MCB localization algorithm based on weighted RSSI and motion prediction

This research presents a comprehensive comparative analysis of SLAM algorithms and Deep Neural Network (DNN)-based Behavior Cloning (BC) navigation in outdoor agricultural environments. The study categorizes SLAM algorithms into laser-based and vision-based approaches, addressing the specific challenges posed by uneven terrain and the similarity between aisles in an orchard farm. The DNN-based BC navigation technique proves efficient, exhibiting reduced human intervention and providing a viable alternative for agricultural navigation. Despite the DNN-based BC navigation approach taking more time to reach its target due to a constant throttle limit for steady speed, the overall performance in terms of driving deviation and human intervention is notable compared to conventional SLAM algorithms. We provide comprehensive evaluation criteria for selecting optimal techniques for outdoor agricultural navigations. The algorithms were tested in three different scenarios: Precision, Speed, and Autonomy. Our proposed performance metric, P, is weighted and normalized. The DNN-based BC algorithm showed the best performance among the others, with a performance of 0.92 in the Precision and Autonomy scenarios. When Speed is more important, the RTAB-Map showed the best score with 0.96. In a case where Autonomy has a higher priority, Gmapping also showed a comparable performance of 0.92 with the DNN-based BC.

Comparative Study on Simulated Outdoor Navigation for Agricultural Robots

In order to improve the positioning accuracy of underground targets, especially the positioning accuracy of moving targets, an improved weighted Monte Carlo positioning algorithm is proposed In the sampling initialization stage, the beacon node gradually constructs the sampling area according to the RSSI size and combines the Monte Carlo method to further narrow the range and improve the sampling success rate In the filtering stage, refer to the sampling area at time and further improve the sample quality at after two filterings In the recollection stage, cooperate with invalid sample sets to reduce the number of recollections and weigh the final samples to improve the positioning accuracy of the nodes to be tested

/pdf/algorithm-of-underground-personnel-positioning-based-on-2zah26czor.pdf

Algorithm of Underground Personnel Positioning Based on Improved Monte Carlo

Correspondenceless scan-to-map-scan matching of 2D panoramic range scans

Self-localization problem of mobile robots is a crucial part of autonomous navigation, especially indoor environments, where the usage of GNSS technology is impossible. There is a lot of studies that focus on this issue, starting from those based on the Monte Carlo Localization (MCL) method, and ending on a variety of SLAM approaches. In majority of them robots explore their operation areas with their sensors and estimate their spatial pose by correlating measurements with known or estimated maps. The UWB (Ultra-Wide Band) technology allows for improvement of these methods since a local positioning system based on it becomes available in indoor environments. This paper proposes an innovative approach to Adaptive Monte Carlo Localization (AMCL) method, which adapts UWB transceivers as a local positioning system. The advantages of applying UWB in AMCL are proved by experimental tests with Automated Guided Vehicle (AGV) in indoor environment. The standard odometry source was substituted by the local positioning system based on UWB transceivers through a modification of original AGV's ROS (robot operating system) setup. Results present that the performance of UWB-AMCL is better than AMCL in most cases. Further advantage of the proposed approach is to enable the standard ROS navigation stack use in cases where no odometry information is available. 

Adaptation of Ultra Wide Band positioning system for Adaptive Monte Carlo Localization

In the process of global positioning of robots, the errors of odometer caused by the abduction of robots and the errors of laser data matching results caused by ambient characteristics will lead to inaccurate Monte Carlo positioning. In this paper, an adaptive Monte Carlo localization algorithm based on position and attitude estimation is proposed. The real-time position and attitude are estimated by matching the scanning data of the laser sensor, and the real-time location is realized by combining the adaptive Monte Carlo algorithm. The results of simulation experiments on ROS show that the algorithm is superior to Monte Carlo algorithm in particle concentration and positioning accuracy, and has good robustness to the abduction problem of robots and the environment without obvious characteristics, which solves the positioning problem caused by the uncertainty of odometer.

Research on Adaptive Monte Carlo Location Method Based on Fusion Posture Estimation

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ting Huang

Author Tools

Chat about Author

Papers

Research on Adaptive Monte Carlo Location Method Based on Fusion Posture Estimation