UcoSLAM: Simultaneous localization and mapping by fusion of keypoints and squared planar markers

Muñoz-Salinas, Rafael; Medina-Carnicer, R.

doi:10.1016/j.patcog.2019.107193

Cited by 110 publications

(72 citation statements)

References 38 publications

(97 reference statements)

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“…When the re-projection error drops below a threshold, the match is added to the optimization problem as a constraint. Another work that exploits the fusion of point features and planar regions, represented as squared fiducial markers in this case, in an environment can be found in [90]. Besides the robustness achieved due to employing point features, utilizing fiducial markers in this system comes with several advantages such as eliminating scale ambiguity, robustness in repetitive environments where distinguishing point features can be challenging, and feature invariance over time.…”

Section: Low-and Middle-level Feature-based Approachesmentioning

confidence: 99%

Feature-based visual simultaneous localization and mapping: a survey

Azzam

Taha²,

Huang

et al. 2020

SN Appl. Sci.

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Visual simultaneous localization and mapping (SLAM) has attracted high attention over the past few years. In this paper, a comprehensive survey of the state-of-the-art feature-based visual SLAM approaches is presented. The reviewed approaches are classified based on the visual features observed in the environment. Visual features can be seen at different levels; low-level features like points and edges, middle-level features like planes and blobs, and high-level features like semantically labeled objects. One of the most critical research gaps regarding visual SLAM approaches concluded from this study is the lack of generality. Some approaches exhibit a very high level of maturity, in terms of accuracy and efficiency. Yet, they are tailored to very specific environments, like feature-rich and static environments. When operating in different environments, such approaches experience severe degradation in performance. In addition, due to software and hardware limitations, guaranteeing a robust visual SLAM approach is extremely challenging. Although semantics have been heavily exploited in visual SLAM, understanding of the scene by incorporating relationships between features is not yet fully explored. A detailed discussion of such research challenges is provided throughout the paper.

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Section: Low-and Middle-level Feature-based Approachesmentioning

confidence: 99%

Feature-based visual simultaneous localization and mapping: a survey

Azzam

Taha²,

Huang

et al. 2020

SN Appl. Sci.

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“…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%

“…The ORB-SLAM uses the same features for tracking, mapping, relocalization and loop closing. This makes the ORB-SLAM system more efficient, simple and reliable as compared to other SLAM techniques.We developed a SLAM by fusion of keypoints and squared planar markers (UcoSLAM) algorithm proposed by Munoz-Salinas et al [11] for the camera based localization system by adding markers to the experiment area. We used Augmented Reality Uco Codes (ArUco) markers for localization and the markers improved the localization accuracy.We proposed hybrid indoor localization systems using an IMU sensor and a smartphone camera.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Poulose

Han

2019

Sensors

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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.

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“…Unavailability of power at the training site, short set up time, and the sheer scale of the environment make these systems impractical to deploy. There are only limited APS, namely, fiducial markers [ 5 ] and passive RFID tags [ 6 ], which uses passive infrastructure and can be calibrated automatically. The passive RFID tags have a limited range, so a vision-based fiducial marker positioning system is more appropriate.…”

Section: Introductionmentioning

confidence: 99%

Improved Position Accuracy of Foot-Mounted Inertial Sensor by Discrete Corrections from Vision-Based Fiducial Marker Tracking

Khan

Clark

Woodward

et al. 2020

Sensors

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In this paper, we present a novel pedestrian indoor positioning system that uses sensor fusion between a foot-mounted inertial measurement unit (IMU) and a vision-based fiducial marker tracking system. The goal is to provide an after-action review for first responders during training exercises. The main contribution of this work comes from the observation that different walking types (e.g., forward walking, sideways walking, backward walking) lead to different levels of position and heading error. Our approach takes this into account when accumulating the error, thereby leading to more-accurate estimations. Through experimentation, we show the variation in error accumulation and the improvement in accuracy alter when and how often to activate the camera tracking system, leading to better balance between accuracy and power consumption overall. The IMU and vision-based systems are loosely coupled using an extended Kalman filter (EKF) to ensure accurate and unobstructed positioning computation. The motion model of the EKF is derived from the foot-mounted IMU data and the measurement model from the vision system. Existing indoor positioning systems for training exercises require extensive active infrastructure installation, which is not viable for exercises taking place in a remote area. With the use of passive infrastructure (i.e., fiducial markers), the positioning system can accurately track user position over a longer duration of time and can be easily integrated into the environment. We evaluated our system on an indoor trajectory of 250 m. Results show that even with discrete corrections, near a meter level of accuracy can be achieved. Our proposed system attains the positioning accuracy of 0.55 m for a forward walk, 1.05 m for a backward walk, and 1.68 m for a sideways walk with a 90% confidence level.

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UcoSLAM: Simultaneous localization and mapping by fusion of keypoints and squared planar markers

Cited by 110 publications

References 38 publications

Feature-based visual simultaneous localization and mapping: a survey

Feature-based visual simultaneous localization and mapping: a survey

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Improved Position Accuracy of Foot-Mounted Inertial Sensor by Discrete Corrections from Vision-Based Fiducial Marker Tracking

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