Application of Particle Filtering Technique for sensor fusion in mobile robotics

Jain, Shikha; Nandy, Sambhunath; Ray, R.K.; Shome, Sankar Nath

doi:10.1109/icma.2011.5986341

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…Multi-sensor fusion can be used for more accurate perception, combining information from multiple sensors (Yeong et al, 2021). Algorithms such as Kalman filter (Sasiadek and Hartana, 2000), Particle Filter (Jain et al, 2011), and Conditional Random Fields (CRF) (Xiao et al, 2015) are used for this purpose. Finally, HAVs apply a combination of mapping and localisation, subsequently using planning algorithms to navigate.…”

Section: Sensementioning

confidence: 99%

Ensuring Safety in Highly Automated Vehicles: A Review of Testing and Validation Methods for Robustness and Reliability

Frischknecht-Gruber,

Ricchiuto,

Reif

et al. 2023

Proceeding of the 33rd European Safety and Reliability Conference

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The future of mobility is set to be reformed as the rapidly increasing use of driver assistance systems and highly automated vehicles (HAVs) show their great potential. The use of deep neural networks in autonomous driving systems has led to significant progress in this area. However, the increase in accidents involving HAVs highlights the need for effective testing and validation methods to increase the overall safety of these vehicles. With many technology companies and manufacturers aiming to put Level 4 and 5 vehicles into operation soon, the safety of HAVs remains a major concern. Rigorous testing and validation against potential failures and misbehaviour are required to ensure the reliability and robustness of these systems. This paper provides an overview of state of the art in testing and evaluation methods for machine learning-based HAVs. A literature review on these topics is provided to give valuable insights to researchers, practitioners and policymakers. As such, the review describes different types of validation, verification and testing methods, including real-world testing, simulation testing, hardware-in-theloop testing, adversarial robustness, and methods used for explainability and interpretability in AI. The advantages and limitations are discussed and current challenges are highlighted. Finally, open research questions and future directions in the field are identified.

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Section: Sensementioning

confidence: 99%

Ensuring Safety in Highly Automated Vehicles: A Review of Testing and Validation Methods for Robustness and Reliability

Frischknecht-Gruber,

Ricchiuto,

Reif

et al. 2023

Proceeding of the 33rd European Safety and Reliability Conference

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“…A common approach to fuse proprioceptive sensors and RFID data employs sequential estimators. Typically, two main steps can be recognized: a prediction step and an update step [9]. To better understand, we can imagine tracking a vehicle moving on a straight line (x-axis), as shown in Fig.…”

Section: Rfid Sensor-fusion With Proprioceptive Sensorsmentioning

confidence: 99%

“…As an alternative, exteroceptive sensors, such as cameras [4], sonars [5], laser range finders (LRFs) [6], or Radio-Frequency (RF) systems [7] can be adopted to circumvent the drift of the estimated trajectory, which is typical of the dead-reckoning approach. To increase the localization accuracy, widespread solutions foresee to combine data from both proprioceptive sensors and exteroceptive ones [8], [9] through sensor-fusion approaches, also known as multisensor data fusion, by means of several different estimation algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Indoor Vehicle Localization Through RFID Technology

2021

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This paper presents a state-of-the-art analysis on the methods suitable for vehicle indoor localization and exploiting the RFID (Radio Frequency IDentification) technology. The survey describes three main categories of vehicle localization systems: (i) solutions exploiting only the RFID technology, (ii) sensor-fusion techniques combining data from RFID systems and proprioceptive sensors, and (iii) sensorfusion techniques combing RFID data with those of other exteroceptive sensors in addition to the RFID system itself. For each method, implementation and methodological details are discussed, by highlighting the applied RFID technology, namely passive HF-RFID, passive UHF-RFID, or any other RFID system. Also, the employed RFID parameters, i.e., tag EPC, RSSI or backscattered phase, are discussed. The survey focuses on the achievable localization performance, also accounting for infrastructure-deployment costs together with complexity and maintenance overhead. Positioning, tracking, navigation and simultaneous localization and mapping (SLAM) issues are here considered. The analysis highlights pros and cons of each method, together with the main challenges and perspectives of RFID-based solutions for vehicle localization.

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“…Dentro de los sistemas estocásticos, la técnica denominada filtro de Kalman, es el método más utilizado para la integración de datos de múltiples sensores [63], dado que permite obtener una estimación de mínima varianza del estado de la variable que se está analizando [64], una variación de este sistema es el filtro de Kalman extendido, el cual posibilita trabajar con sistemas no lineales, donde se busca obtener una aproximación más certera de las medidas de los sensores [65,66]. Otro de los filtros que es utilizado es el llamado filtro de partículas, el cual permite resolver dificultades de incertidumbre y no linealidad en las medidas obtenidas [67]; para otras investigaciones se hizo uso de estrategias de modelos probabilísticos, dado que se puede incorporar la incertidumbre de la medida que se ha tomado [68], permitiendo calcular una distribución de probabilidad sobre lo que tal vez fuera el caso en el mundo real, en lugar de generar un único valor.…”

Section: Estrategias De Adquisición De Información De Los Sensoresunclassified

Navegación de robots móviles en entornos con discontinuidades: una revisión

2018

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Se realizó una revisión del estado del arte en torno a la navegación de robots móviles con ruedas, donde particularmente se analizan algunas técnicas usadas en el tratamiento de los hitos, particularidades o discontinuidades que se puedan encontrar en una posible ruta óptima, haciendo principal énfasis en la navegación en entornos con pasillos y puertas, dado que son elementos importantes cuando se habla de navegación en entornos interiores como casa, edificios, fábricas, etc., se analizan temas desde la adquisición de la señal, estrategias para el procesamiento de la información y las respuestas obtenidas.

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Application of Particle Filtering Technique for sensor fusion in mobile robotics

Cited by 7 publications

References 12 publications

Ensuring Safety in Highly Automated Vehicles: A Review of Testing and Validation Methods for Robustness and Reliability

Ensuring Safety in Highly Automated Vehicles: A Review of Testing and Validation Methods for Robustness and Reliability

A Survey on Indoor Vehicle Localization Through RFID Technology

Navegación de robots móviles en entornos con discontinuidades: una revisión

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