Review and classification of vision‐based localisation techniques in unknown environments

Ben-Afia, Amani; Deambrogio, Lina; Salos, Daniel; Escher, Anne-Christine; Macabiau, Christophe; Soulier, Laurent; Gripon, Vincent

doi:10.1049/iet-rsn.2013.0389

Cited by 34 publications

(19 citation statements)

References 69 publications

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“…The main advantages of this method lie in its ease of implementation, low cost, fast computation and improved accuracy. Currently, it has been proven that vision could be a promising navigation sensor that provides accurate information about position and orientation [ 18 ]. Cameras have the advantage of providing an extensive amount of information while having a low weight, limited power consumption, low cost and reasonable size.…”

Section: Introductionmentioning

confidence: 99%

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Alatise

Hancke

2017

Sensors

166

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Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).

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Section: Introductionmentioning

confidence: 99%

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Alatise

Hancke

2017

Sensors

166

View full text Add to dashboard Cite

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“…Location-based services rely on location sensing systems that use various technologies either individually or in combination. These technologies include, GNSS, mobile networks [ 36 ], WiFi [ 1 , 37 ], inertial sensors [ 38 , 39 , 40 ], infrared (IR) light [ 41 ], visible light communication (VLC) [ 42 , 43 ], ultrasound [ 44 , 45 ], audible sound [ 46 ], radio frequency identification (RFID) [ 47 ], bluetooth (BT) [ 48 ], ultra wideband (UWB) [ 16 , 49 ], Zigbee [ 50 ], vision (image/video) based techniques [ 51 , 52 , 53 , 54 ] and frequency modulation (FM) [ 55 ]. Frequently, several technologies are combined.…”

Section: Factors Affecting the Quality Of Location Informationmentioning

confidence: 99%

Location Information Quality: A Review

Ranasinghe

Kray

2018

Sensors

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The quality of location information is an important factor for location-based services (LBSs). In the literature, the quality of location information has been defined in different ways based on varying sets of aspects. The objectives of this paper are to review existing literature discussing location information quality and to provide a consistent framework for describing and dealing with location information quality. In particular, we review existing literature on different aspects of location information quality and on factors that affect location sensing technologies (and thus location information quality). Based on this review, we also propose a simple model for describing location information quality and a classification of the strategies for dealing with variations in the quality of location information. Designers of location sensing systems can use this model as a standard vocabulary for describing the quality of location information. The classification of strategies can be used by developers of LBSs apps to design alternative strategies for dealing with location information quality on three levels: sensor-level, algorithm-level, and application-level, which are aligned with the Location Stack model.

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“…For real time 3D environment exploration the trade off is in between the mapping and localization methods. Here using the visual data, the construction of the map and calculation of a precise trajectory inside the map can be done by using visual slam [4] [10]. Slam problem is usually defined in probabilistic form.…”

Section: Related Workmentioning

confidence: 99%

Real-time graph-based SLAM in unknown environments using a small UAV

Annaiyan

Olivares-Méndez

Voos

2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-Autonomous navigation of small Unmanned Aerial Vehicles (UAVs) in cluttered environments is still a challenging problem. In this work, we present an approach based on graph slam and loop closure detection for online mapping of unknown outdoor environments using a small UAV. Here, we used an onboard front facing stereo camera as the primary sensor. The data extracted by the cameras are used by the graph-based slam algorithm to estimate the position and create the graph-nodes and construct the map. To avoid multiple detections of one object as different objects and to identify re-visited locations, a loop closure detection is applied with optimization algorithm using the g2o toolbox to minimize the error. Furthermore, 3D occupancy map is used to represent the environment. This technique is used to save memory and computational time for the online processing. Real experiments are conducted in outdoor cluttered and open field environments.The experiment results show that our presented approach works under real time constraints, with an average time to process the nodes of the 3D map is 17.79ms

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Review and classification of vision‐based localisation techniques in unknown environments

Cited by 34 publications

References 69 publications

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Location Information Quality: A Review

Real-time graph-based SLAM in unknown environments using a small UAV

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