Improving Robot Localization Using Doppler-Based Variable Sensor Covariance Calculation

Fariña, Bibiana; Toledo, Jonay; Estévez, J. I.; Sánchez, Leopoldo

doi:10.3390/s20082287

Cited by 12 publications

(13 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The study by Fariña, B. et al has shed more light on an autonomous wheelchair localization. In the article [2], the authors describe a localization module that includes a combination of various sensors. The sensors used in the module feature variable covariance estimations in order to yield an accurate final localization.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Sensors and System for Vehicle Navigation

Stateczny

Kazimierski

Burdziakowski

2022

Sensors

View full text Add to dashboard Cite

show abstract

Section: Overview Of Contributionsmentioning

confidence: 99%

Sensors and System for Vehicle Navigation

Stateczny

Kazimierski

Burdziakowski

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…En desarrollos previamente publicados por nuestro grupo [6] el modelo se basaba en un modelo 2D de movimiento omnidireccional. Este modelo se basa en la suposición que la velocidad no cambia en un periodo de tiempo.…”

Section: Sistema De Localizaciónunclassified

Covarianza dinamica en un sistema odometrico real para la mejora de la localizacion con sensores a bordo

Toledo¹,

Fariña²,

Sánchez³

2022

XLIII Jornadas De Automática: Libro De Actas: 7, 8 Y 9 De Septiembre De 2022, Logroño (La Rioja)

View full text Add to dashboard Cite

En este artículo se presenta una estrategia de fusión sensorial para mejorar el sistema de localización de una silla de ruedas inteligente. Para conseguir una buena localización, la fusión de la información proporcionada por varios sensores es necesaria, en este caso telémetro laser, unidad de medida inercial y sistema odométrico. Los datos de cada sensor deben estar caracterizados por su covarianza. La estrategia clásica consiste en la asignación de una covarianza estática para el sensor odométrico, sin embargo con una covarianza dinámica, que realmente caracterice al sensor en tiempo real, se puede mejorar el resultado final. En este artículo se compara el resultado de la covarianza estática, frente a una calculada dinámicamente utilizando otro sensor externo. También se compara un modelo de fusión sensorial basado en velocidad constante, frente a otro modelo donde se especifica la odometría como sistema de evolución interno en el filtro de Kalman

show abstract

“…FMCW dopper-enabled radars have been shown in Reference [29] to be beneficial. In this work, however, we consider range-only radar.…”

Section: Radar-based Mapping and Localisationmentioning

confidence: 99%

kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation

Martini

Gadd

Newman

2020

Sensors

View full text Add to dashboard Cite

This paper presents a novel two-stage system which integrates topological localisation candidates from a radar-only place recognition system with precise pose estimation using spectral landmark-based techniques. We prove that the—recently available—seminal radar place recognition (RPR) and scan matching sub-systems are complementary in a style reminiscent of the mapping and localisation systems underpinning visual teach-and-repeat (VTR) systems which have been exhibited robustly in the last decade. Offline experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community with performance comparing favourably with and even rivalling alternative state-of-the-art radar localisation systems. Specifically, we show the long-term durability of the approach and of the sensing technology itself to autonomous navigation. We suggest a range of sensible methods of tuning the system, all of which are suitable for online operation. For both tuning regimes, we achieve, over the course of a month of localisation trials against a single static map, high recalls at high precision, and much reduced variance in erroneous metric pose estimation. As such, this work is a necessary first step towards a radar teach-and-repeat (RTR) system and the enablement of autonomy across extreme changes in appearance or inclement conditions.

show abstract

Improving Robot Localization Using Doppler-Based Variable Sensor Covariance Calculation

Cited by 12 publications

References 25 publications

Sensors and System for Vehicle Navigation

Sensors and System for Vehicle Navigation

Covarianza dinamica en un sistema odometrico real para la mejora de la localizacion con sensores a bordo

kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation

Contact Info

Product

Resources

About