Self-Localization in Highly Dynamic Environments Based on Dual-Channel Unscented Particle Filter

Chen, Hao; Liu, Chengju; Chen, Qijun

doi:10.1017/s0263574720001046

Robotica

2020

DOI: 10.1017/s0263574720001046

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Self-Localization in Highly Dynamic Environments Based on Dual-Channel Unscented Particle Filter

Hao Chen

Chengju Liu

Qijun Chen

Abstract: SUMMARY Self-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertia… Show more

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Cited by 4 publications

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References 37 publications

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“…The use of simulation tools is a common practice in developing MAS to anticipate the learning level and the cognitive agents' action performance [4][5][6][7]. For example, tools are used in the robot soccer domain to support simulation and setplays definition [8][9][10]. However, these tools have a limited set of behaviors available to use.…”

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confidence: 99%

Towards Automatic Code Generation for Robotic Soccer Behavior Simulation

Sales,

Fontes Magalhães Mascarenhas,

Simões

et al. 2024

J Intell Robot Syst

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Multi-Agent Systems (MAS) are an Artificial Intelligence (AI) branch where agents handle distributed nature tasks in a cooperative system. MAS is widely used in robotic systems in scenarios where multiple robots must cooperate. In this direction, the robot soccer domain has been used as a test bed to stimulate research in this area, as it reproduces some important features of these systems, such as coordination. Each soccer team member is an agent whose behavior must be coordinated with the other team members cooperating to win the game. Simulation tools are frequently used in this context to create rehearsed plays, called setplays, during team training. However, these tools generally have a limited set of behaviors, e.g., kicking, available to use in setplays, and new behaviors must be manually implemented. This implementation requires knowledge of specific source codes and a significant programming effort, in addition to leaving the behavior coupled and dependent on the tool. This work proposes the Robot Soccer Behavior Generator (RoboSocBG), a solution to develop new behaviors in the context of simulated soccer robots. It uses Model-Driven Development (MDD), an approach that enables the specification of behavior platform-independent models and code generation in specific tools. The solution was tested in our laboratory and validated in a case study. The results evidenced its feasibility to generate code in different platforms.

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mentioning

confidence: 99%

Towards Automatic Code Generation for Robotic Soccer Behavior Simulation

Sales,

Fontes Magalhães Mascarenhas,

Simões

et al. 2024

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

An efficient LiDAR-based localization method for self-driving cars in dynamic environments

Zhang¹,

Wang²,

Jiang³

et al. 2021

Robotica

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Real-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method.

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A visual SLAM-based lightweight multi-modal semantic framework for an intelligent substation robot

Li,

Gu,

et al. 2024

Robotica

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Visual simultaneous localisation and mapping (vSLAM) has shown considerable promise in positioning and navigating across a variety of indoor and outdoor settings, significantly enhancing the mobility of robots employed in industrial and everyday services. Nonetheless, the prevalent reliance of vSLAM technology on the assumption of static environments has led to suboptimal performance in practical implementations, particularly in unstructured and dynamically noisy environments such as substations. Despite advancements in mitigating the influence of dynamic objects through the integration of geometric and semantic information, existing approaches have struggled to strike an equilibrium between performance and real-time responsiveness. This study introduces a lightweight, multi-modal semantic framework predicated on vSLAM, designed to enable intelligent robots to adeptly navigate the dynamic environments characteristic of substations. The framework notably enhances vSLAM performance by mitigating the impact of dynamic objects through a synergistic combination of object detection and instance segmentation techniques. Initially, an enhanced lightweight instance segmentation network is deployed to ensure both the real-time responsiveness and accuracy of the algorithm. Subsequently, the algorithm’s performance is further refined by amalgamating the outcomes of detection and segmentation processes. With a commitment to maximising performance, the framework also ensures the algorithm’s real-time capability. Assessments conducted on public datasets and through empirical experiments have demonstrated that the proposed method markedly improves both the accuracy and real-time performance of vSLAM in dynamic environments.

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Self-Localization in Highly Dynamic Environments Based on Dual-Channel Unscented Particle Filter

Cited by 4 publications

References 37 publications

Towards Automatic Code Generation for Robotic Soccer Behavior Simulation

Towards Automatic Code Generation for Robotic Soccer Behavior Simulation

An efficient LiDAR-based localization method for self-driving cars in dynamic environments

A visual SLAM-based lightweight multi-modal semantic framework for an intelligent substation robot

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