Implementation of Mobile Sensor Navigation System Based on Adaptive Monte Carlo Localization

Wasisto, Isro; Istiqomah, Novera; Trisnawan, I Kadek Nuary; Jati, Agung Nugroho

doi:10.1109/ic3ina48034.2019.8949581

Cited by 16 publications

(7 citation statements)

References 2 publications

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“…Due to the fact that the method proposed in this work requires the system to run for a certain distance to estimate the initial pose, similar to some Monte Carlo-based place recognition methods [ 8 , 9 ], this paper evaluates the required distance. Figure 7 shows the HMM running distance; it can be seen that the maximum distance is less than 16 m. According to actual engineering experience, the convergence distance of the 3D Monte Carlo localization method is usually around 10–50 m. Thus, the proposed method only requires a short running distance to achieve good results.…”

Section: Methodsmentioning

confidence: 99%

“…Chen and Vizzo [ 8 ] proposed a Monte Carlo localization framework and utilized the Monte Carlo method to match the current LiDAR scan to a pre-built map for initial localization. Wasisto, Isro, and Istiqomah [ 9 ] utilized the adaptive Monte Carlo localization method for initial localization. However, the Monte Carlo method is computationally intensive; the real-time performance could be better.…”

Section: Related Workmentioning

confidence: 99%

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Place Recognition through Multiple LiDAR Scans Based on the Hidden Markov Model

Gui,

Zeng,

Luo

et al. 2024

Sensors

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Autonomous driving systems for unmanned ground vehicles (UGV) operating in enclosed environments strongly rely on LiDAR localization with a prior map. Precise initial pose estimation is critical during system startup or when tracking is lost, ensuring safe UGV operation. Existing LiDAR-based place recognition methods often suffer from reduced accuracy due to only matching descriptors from individual LiDAR keyframes. This paper proposes a multi-frame descriptor-matching approach based on the hidden Markov model (HMM) to address this issue. This method enhances the place recognition accuracy and robustness by leveraging information from multiple frames. Experimental results from the KITTI dataset demonstrate that the proposed method significantly enhances the place recognition performance compared with the scan context-based single-frame descriptor-matching approach, with an average performance improvement of 5.8% and with a maximum improvement of 15.3%.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Place Recognition through Multiple LiDAR Scans Based on the Hidden Markov Model

Gui,

Zeng,

Luo

et al. 2024

Sensors

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“…Localization for each robot is achieved using the AMCL system [ 34 , 35 ], which uses a particle filter to determine the pose of the robot given a known map. Usually, this implies an indoor environment, such as a static and structured environment that can be represented by a map, but this is also the case with a vineyard field with its well-defined rows.…”

Section: Methodsmentioning

confidence: 99%

Coordinated Navigation of Two Agricultural Robots in a Vineyard: A Simulation Study

Lytridis

Bazinas

Pachidis

et al. 2022

Sensors

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The development of an effective agricultural robot presents various challenges in actuation, localization, navigation, sensing, etc., depending on the prescribed task. Moreover, when multiple robots are engaged in an agricultural task, this requires appropriate coordination strategies to be developed to ensure safe, effective, and efficient operation. This paper presents a simulation study that demonstrates a robust coordination strategy for the navigation of two heterogeneous robots, where one robot is the expert and the second robot is the helper in a vineyard. The robots are equipped with localization and navigation capabilities so that they can navigate the environment and appropriately position themselves in the work area. A modular collaborative algorithm is proposed for the coordinated navigation of the two robots in the field via a communications module. Furthermore, the robots are also able to position themselves accurately relative to each other using a vision module in order to effectively perform their cooperative tasks. For the experiments, a realistic simulation environment is considered, and the various control mechanisms are described. Experiments were carried out to investigate the robustness of the various algorithms and provide preliminary results before real-life implementation.

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“…Monte Carlo method has been applied to sensor networks in both location [12] and energy balance [13] issues. In these references, the authors presented a systemic approach based on random numbers to achieve convergence in iterative algorithms at a convergence rate close to 1/N.…”

Section: Related Workmentioning

confidence: 99%

“…CO obs (GROUP 0) = 0.68 ppm CO obs (GROUP The result obtained through Equation ( 13) (resolution) indicates the total dispersion of sensors present in the groups indicated in Figure 7's example. From this, the genetic algorithm has the final objective of matching the groups and routes, resulting in the smallest sum of sensor network variances, indicated in Equation (12).…”

Section: Hexagonal Sensor Network Meshmentioning

confidence: 99%

Network Optimization of Carbon Monoxide Sensor Nodes in the Metropolitan Region of São Paulo

Borges,

Lopes,

da Silva

2023

Electronics

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Air pollution is one of the biggest problems affecting large urban areas. Better monitoring of regions suffering from this type of pollution is in the interest of public health. Although many cities employ sensors to monitor air pollution, a current concern is how to establish the ideal number of sensors to monitor a given geographical region. To address this concern, this research proposes a method to optimize the number of sensors in an air pollution monitoring network to cover a given region efficiently and precisely and uses the metropolitan region of São Paulo, Brazil, and CO sensors as an example. The model of Fragmentation into Groups via Routes is proposed to distribute sensors within micro-regions that display similar air pollution characteristics. A network of virtual sensors is created, and the output of each sensor is established using a method of spatial interpolation called IDW. To identify the optimum sensor configuration, a genetic algorithm is used to assess the topology with the lowest variance of data spread. A lesser number of sensor stations to be treated leads to faster responses to sudden changes in urban conditions. Therefore, municipality authorities can take quick measures to improve the population’s wellness.

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Implementation of Mobile Sensor Navigation System Based on Adaptive Monte Carlo Localization

Cited by 16 publications

References 2 publications

Place Recognition through Multiple LiDAR Scans Based on the Hidden Markov Model

Place Recognition through Multiple LiDAR Scans Based on the Hidden Markov Model

Coordinated Navigation of Two Agricultural Robots in a Vineyard: A Simulation Study

Network Optimization of Carbon Monoxide Sensor Nodes in the Metropolitan Region of São Paulo

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