Crowd Map: Accurate Reconstruction of Indoor Floor Plans from Crowdsourced Sensor-Rich Videos

Chen, Si; Li, Muyuan; Ren, Kui; Qiao, Chunming

doi:10.1109/icdcs.2015.9

Cited by 60 publications

(32 citation statements)

References 33 publications

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“…CrowdMap [3] and Jigsaw [7] are example solutions of crowdsourced indoor floor plan reconstruction. The first one uses crowdsourced video and inertial sensor data, while the latter takes crowdsourced photos as input.…”

Section: Background and Related Workmentioning

confidence: 99%

“…While walking, the volunteers always carried a smartphone on which the accelerometer and gyroscope readings were being collected. In addition to the traces, a 3D point cloud of the indoor space in question was generated from around 2,000 photos collected in advance by different users 3 We created an occupancy grid map from the 3D point cloud and used it for experiment. The map is visualized in Figure 2b.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…We tighten the constraint in order to differentiate the collisions due to inaccurate stride length 3. Detailed description of these photos and the generated 3D point cloud is presented in our previous work[4] 4.…”

mentioning

confidence: 99%

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Indoor Tracking Using Crowdsourced Maps

Jiang¹,

Xiao²,

Ou³

et al. 2016

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

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Using crowdsourced visual and inertial sensor data for indoor mapping has attracted much attention in recent years. Nevertheless, the opportunities and challenges of indoor tracking using crowdsourced maps have not been fully explored. In this work, we aim at tackling the challenges due to incomplete obstacle information in crowdsourced indoor maps, especially at the initialization stage of crowdsourcing. We propose a novel solution for particle-filtering-based indoor tracking, using the crowdsourced maps derived from image-based 3D point clouds. Our solution enhances particle filtering with density-based collision detection and history-based particle regeneration. Evaluation with real user traces demonstrates that our solution outperforms the state-of-the-art. In particular, it reduces the average distance error of indoor tracking by 47% when using crowdsourced 3D point clouds.

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Section: Background and Related Workmentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

See 1 more Smart Citation

Indoor Tracking Using Crowdsourced Maps

Jiang¹,

Xiao²,

Ou³

et al. 2016

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

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“…Error analysis will be presented in the next section. The field of view λ = 2arctan( w 2 f ), where w is the width of the image sensor and f is the focal length, both of which can obtained from the Android API [22][23][24]. During the experiment, all photos have the same field of view.…”

Section: Data-unit Acquisitionmentioning

confidence: 99%

The Minimum Selection of Crowdsourcing Images under the Resource Budget

Song¹,

Zhao²,

Long³

2018

Symmetry

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Images crowdsourcing of mobile devices can be applied to many real-life application scenarios. However, this type of scenario application often faces issues such as the limitation of bandwidth, insufficient storage space, and the processing capability of CPU. These lead to only a few photos that can be crowdsourced. Therefore, it is a great challenge to use a limited number of resources to select photos and make it possible to cover the target area maximally. In this paper, the geographic and geometric information of the photo called data-unit is used to cover the target area as much as possible. Compared with traditional content-based image delivery methods, the network delay and computational costs can be greatly reduced. In the case of resource constraints, this paper uses the utility of photos to measure the coverage of the target area, and improves a photo utility calculation method based on data-unit. In the meantime, this paper proposes the minimum selection problem of images under the coverage requirements, and designs a selection algorithm based on greedy strategies. Compared with other traditional random selection algorithms, the results prove the effectiveness and superiority of the minimum selection algorithm.

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“…For example, if one person makes a free turn not at a special location, this method may match this activity to an incorrect location. Much effort has been made to improve the performance of trajectory estimation [31][32][33][34]. For example, the trajectory alignment and calibration method proposed in [32] can align a crowdsourcing trajectory into a coordinate system by using a foot-mounted inertial sensor and Wi-Fi RSS measurements.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization

Liu

Zhang

et al. 2019

Remote Sensing

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One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an accurate visual-inertial integrated geo-tagging method that can be used to collect fingerprints and construct the radio map by exploiting the crowdsourced trajectory of smartphone users. By integrating multisource information from the smartphone sensors (e.g., camera, accelerometer, and gyroscope), this system can accurately reconstruct the geometry of trajectories. An algorithm is proposed to estimate the spatial location of trajectories in the reference coordinate system and construct the radio map and geo-tagged image database for indoor positioning. With the help of several initial reference points, this algorithm can be implemented in an unknown indoor environment without any prior knowledge of the floorplan or the initial location of crowdsourced trajectories. The experimental results show that the average calibration error of the fingerprints is 0.67 m. A weighted k-nearest neighbor method (without any optimization) and the image matching method are used to evaluate the performance of constructed multisource database. The average localization error of received signal strength (RSS) based indoor positioning and image based positioning are 3.2 m and 1.2 m, respectively, showing that the quality of the constructed indoor radio map is at the same level as those that were constructed by site surveying. Compared with the traditional site survey based positioning cost, this system can greatly reduce the human labor cost, with the least external information.

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Crowd Map: Accurate Reconstruction of Indoor Floor Plans from Crowdsourced Sensor-Rich Videos

Cited by 60 publications

References 33 publications

Indoor Tracking Using Crowdsourced Maps

Indoor Tracking Using Crowdsourced Maps

The Minimum Selection of Crowdsourcing Images under the Resource Budget

An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization

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