Image-based camera localization: an overview

Wu, Yihong; Tang, Fulin; Li, Heping

doi:10.1186/s42492-018-0008-z

Cited by 89 publications

(44 citation statements)

References 121 publications

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“…A comparative study of IMU and camera based localization can be seen in [102]. Most recent work on hybrid localization is discussed in [103,104,105,106,107,108,109]. Among these studies, the hybrid indoor localization can be used for better performance and which reduces IMU sensor and camera position errors.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Poulose

Han

2019

Sensors

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“…position and orientation, through the use of a sensing system, e.g., GPS, infrared or ultrasonic distance sensor, lidar, accelerometer / compass / magnetometer or a camera. Among these, the image-based camera localization is the most flexible and low cost approach [6], but at the same time it is the most complex one. As a result, many research works have been involved with developing novel techniques for dealing with the peculiarities on this domain.…”

Section: Related Workmentioning

confidence: 99%

Single Vision-Based Self-Localization for Autonomous Robotic Agents

Avgeris

Spatharakis

Αθανασόπουλος

et al. 2019

2019 7th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)

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We present a single vision-based, selflocalization method for autonomous mobile robots in a known, indoor environment. This absolute localization method is landmark assisted, therefore, we propose an algorithm that requires the extraction of a single landmark feature i.e., the length of a known edge. Our technique is based on measuring the distance from two distinct, arbitrarily positioned landmarks in the robot's environment, the locations of which are known a priori. A single camera vision system is used to perform distance estimation. The developed framework is applied to tracking a robot's pose, i.e., its position and orientation, in a Cartesian coordinate system. The position of the robot is estimated using a bilateration method, while its orientation calculation utilizes tools from projective geometry. The validity and feasibility of the approach are demonstrated through experiments.

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“…A triangulation stage is operated only on inliers that are visible in the two images at times (t−1) and (t−2), and in the current image at time (t) to estimate the 3D points boldXn in the Navigation frame. To perform pose estimation using a calibrated camera, PnP algorithms constitute one of the most suitable solutions [45]. The camera pose is computed using the following formulation:truex˜i=boldK[]Rnc|tncboldXfalse˜ntnc=−RncCnwhere boldCn is the position of the camera’s optical center in the Navigation frame.…”

Section: Scaled Monocular Visual Odometrymentioning

confidence: 99%

Solving Monocular Visual Odometry Scale Factor with Adaptive Step Length Estimates for Pedestrians Using Handheld Devices

Antigny

Uchiyama

Servières

et al. 2019

Sensors

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The urban environments represent challenging areas for handheld device pose estimation (i.e., 3D position and 3D orientation) in large displacements. It is even more challenging with low-cost sensors and computational resources that are available in pedestrian mobile devices (i.e., monocular camera and Inertial Measurement Unit). To address these challenges, we propose a continuous pose estimation based on monocular Visual Odometry. To solve the scale ambiguity and suppress the scale drift, an adaptive pedestrian step lengths estimation is used for the displacements on the horizontal plane. To complete the estimation, a handheld equipment height model, with respect to the Digital Terrain Model contained in Geographical Information Systems, is used for the displacement on the vertical axis. In addition, an accurate pose estimation based on the recognition of known objects is punctually used to correct the pose estimate and reset the monocular Visual Odometry. To validate the benefit of our framework, experimental data have been collected on a 0.7 km pedestrian path in an urban environment for various people. Thus, the proposed solution allows to achieve a positioning error of 1.6–7.5% of the walked distance, and confirms the benefit of the use of an adaptive step length compared to the use of a fixed-step length.

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Image-based camera localization: an overview

Cited by 89 publications

References 121 publications

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera

Single Vision-Based Self-Localization for Autonomous Robotic Agents

Solving Monocular Visual Odometry Scale Factor with Adaptive Step Length Estimates for Pedestrians Using Handheld Devices

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