Occupancy grid based graph-SLAM using the distance transform, SURF features and SGD

Gil, Arturo; Juliá, Miguel; Reinoso, Óscar

doi:10.1016/j.engappai.2014.12.010

Cited by 11 publications

(7 citation statements)

References 23 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Estructura y trayectoria generadas por el método DEG Con respecto a los datos comparativos obtenidos, tanto el método REG como el método DEG lograron una cobertura del 100% del área en todas las pruebas realizadas; esto, gracias al conocimiento acumulativo generado por ambos métodos sobre qué áreas no se fueron exploradas completamente. Para medir este parámetro, el ambiente de trabajo se dividió en celdas de exploración como lo sugiere el método presentado en (Gil et al, 2015); sin embargo, para nuestro caso, la división del ambiente sólo se utilizó para determinar qué celdas fueron exploradas, lo que nos permitió conocer la superficie total examinada por los métodos (Figura 8).…”

Section: Resultados Experimentalesunclassified

Sobre la mejora esperada de la estimación de la odometría en Exploración Integrada

Palacios

López

2020

Rev. iberoam. autom. inform. ind.

View full text Add to dashboard Cite

El problema de Exploración integrada es la nueva tendencia en la construcción de mapas de ambientes desconocidos; en ella, se integra el viejo paradigma de la localización y mapeo simultáneos (SLAM) con la planificación de movimientos necesarios, para que esta tarea sea realizada de forma autónoma. Sin embargo, aunque el control de movimientos es una parte esencial de este paradigma, los trabajos encontrados en la literatura se han limitado a desarrollar estrategias que mejoren los tiempos de recorridos y la cobertura del ambiente, dejado de lado el impacto que estos puede tener sobre la odometría del robot y, en consecuencia, sobre los requerimientos de los algoritmos de localización. De lo anterior, en este documento se presenta una nueva forma eficiente de exploración de ambientes para el problema de SLAM, que tiene como objetivo mejorar los tiempos de exploración y maximizar la cobertura del área de trabajo, pero además el de minimizar el error odométrico acumulado para simplificar el proceso de localización.

show abstract

Section: Resultados Experimentalesunclassified

Sobre la mejora esperada de la estimación de la odometría en Exploración Integrada

Palacios

López

2020

Rev. iberoam. autom. inform. ind.

View full text Add to dashboard Cite

show abstract

“…In SLAM based localization, we create a map of the experiment area and at the same time locate the camera position. The SLAM technique is classified as extended Kalman filter (EKF) SLAM [65,66], FastSLAM [67], low dimensionality (L)-SLAM [68], GraphSLAM [69], Occupancy Grid SLAM [70,71,72], distributed particle (DP)-SLAM [73], parallel tracking and mapping (PTAM) [74], stereo parallel tracking and mapping (S-PTAM) [75], dense tracking and mapping (DTAM) [76,77], incremental smoothing and mapping (iSAM) [78], large-scale direct (LSD)-SLAM [79], MonoSLAM [80], collaborative visual SLAM (CoSLAM) [81], SeqSLAM [82], continuous time (CT)-SLAM [83], UcoSLAM [11], RGB-D SLAM [84] and ORB SLAM [85,86,87]. In this paper, we used ORB SLAM and UcoSLAM for camera based localization.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Poulose

Han

2019

Sensors

View full text Add to dashboard Cite

Smartphone camera or inertial measurement unit (IMU) sensor-based systems can be independently used to provide accurate indoor positioning results. However, the accuracy of an IMU-based localization system depends on the magnitude of sensor errors that are caused by external electromagnetic noise or sensor drifts. Smartphone camera based positioning systems depend on the experimental floor map and the camera poses. The challenge in smartphone camera-based localization is that accuracy depends on the rapidness of changes in the user’s direction. In order to minimize the positioning errors in both the smartphone camera and IMU-based localization systems, we propose hybrid systems that combine both the camera-based and IMU sensor-based approaches for indoor localization. In this paper, an indoor experiment scenario is designed to analyse the performance of the IMU-based localization system, smartphone camera-based localization system and the proposed hybrid indoor localization system. The experiment results demonstrate the effectiveness of the proposed hybrid system and the results show that the proposed hybrid system exhibits significant position accuracy when compared to the IMU and smartphone camera-based localization systems. The performance of the proposed hybrid system is analysed in terms of average localization error and probability distributions of localization errors. The experiment results show that the proposed oriented fast rotated binary robust independent elementary features (BRIEF)-simultaneous localization and mapping (ORB-SLAM) with the IMU sensor hybrid system shows a mean localization error of 0.1398 m and the proposed simultaneous localization and mapping by fusion of keypoints and squared planar markers (UcoSLAM) with IMU sensor-based hybrid system has a 0.0690 m mean localization error and are compared with the individual localization systems in terms of mean error, maximum error, minimum error and standard deviation of error.

show abstract

“…The former is known for its semantic interpretability and low complexity, while the latter is computational resources dependant and holds the promise of high accuracy. These two types of maps are generally components of simultaneous localisation and mapping (SLAM) solutions (Gil et al, 2015, Li et al, 2019. Therefore, they inherit the challenges faced by SLAM approaches, such as localisation accuracy degradation when the uncertainty of sensor measurements increases.…”

Section: Introductionmentioning

confidence: 99%

Feature-refined box particle filtering for autonomous vehicle localisation with OpenStreetMap

Wang

Mihaylova²,

Xu³

et al. 2021

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Occupancy grid based graph-SLAM using the distance transform, SURF features and SGD

Cited by 11 publications

References 23 publications

Sobre la mejora esperada de la estimación de la odometría en Exploración Integrada

Sobre la mejora esperada de la estimación de la odometría en Exploración Integrada

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Feature-refined box particle filtering for autonomous vehicle localisation with OpenStreetMap

Contact Info

Product

Resources

About