Drift-Aware Monocular Localization Based on a Pre-Constructed Dense 3D Map in Indoor Environments

Feng, Guanyuan; Ma, Lin; Tan, Xiaoyang; Qin, Danyang

doi:10.3390/ijgi7080299

Cited by 8 publications

(8 citation statements)

References 39 publications

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“…Radio signal-based localisation methods [11], such as WiFi [12], radio frequency identification [13], ultra-wideband (UWB) [10,14], are used for completion of GPS in an indoor environment. However, the signal strength is easily affected by walls or moving objects, resulting in localisation inaccuracy or failure [15]. Moreover, it is not flexible and cheap to use these methods because of the fixed anchor points.…”

Section: Related Workmentioning

confidence: 99%

Drift‐free localisation using prior cross‐source map for indoor low‐light environments

Tao

Dai

Kong

et al. 2023

IET Cyber-Syst and Robotics

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In this study, a localisation system without cumulative errors is proposed. First, depth odometry is achieved only by using the depth information from the depth camera. Then the point cloud cross-source map registration is realised by 3D particle filtering to obtain the pose of the point cloud relative to the map. Furthermore, we fuse the odometry results with the point cloud to map registration results, so the system can operate effectively even if the map is incomplete. The effectiveness of the system for long-term localisation, localisation in the incomplete map, and localisation in low light through multiple experiments on the self-recorded dataset is demonstrated. Compared with other methods, the results are better than theirs and achieve high indoor localisation accuracy.

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Section: Related Workmentioning

confidence: 99%

Drift‐free localisation using prior cross‐source map for indoor low‐light environments

Tao

Dai

Kong

et al. 2023

IET Cyber-Syst and Robotics

View full text Add to dashboard Cite

show abstract

“…In addition, with the emergence of some fast image matching algorithms (e.g., ORB, SURF, and so on) and clustering algorithms (e.g., Random forests, SVM, and so on), the real-time performance of visual positioning methods has been studied more and more [18,[21][22][23][24][25]. In [26], the PnP method was used to solve the motion of a calibrated camera through a set of n 3D points in the world and their corresponding 2D projections in the image. A continuous camera pose estimation method for indoor monocular cameras was proposed, which improved the camera pose estimation accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Precise Indoor Visual Positioning Approach Using a Built Image Feature Database and Single User Image from Smartphone Cameras

Chen

Liao

et al. 2020

Remote Sensing

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Indoor visual positioning is a key technology in a variety of indoor location services and applications. The particular spatial structures and environments of indoor spaces is a challenging scene for visual positioning. To address the existing problems of low positioning accuracy and low robustness, this paper proposes a precision single-image-based indoor visual positioning method for a smartphone. The proposed method includes three procedures: First, color sequence images of the indoor environment are collected in an experimental room, from which an indoor precise-positioning-feature database is produced, using a classic speed-up robust features (SURF) point matching strategy and the multi-image spatial forward intersection. Then, the relationships between the smartphone positioning image SURF feature points and object 3D points are obtained by an efficient similarity feature description retrieval method, in which a more reliable and correct matching point pair set is obtained, using a novel matching error elimination technology based on Hough transform voting. Finally, efficient perspective-n-point (EPnP) and bundle adjustment (BA) methods are used to calculate the intrinsic and extrinsic parameters of the positioning image, and the location of the smartphone is obtained as a result. Compared with the ground truth, results of the experiments indicate that the proposed approach can be used for indoor positioning, with an accuracy of approximately 10 cm. In addition, experiments show that the proposed method is more robust and efficient than the baseline method in a real scene. In the case where sufficient indoor textures are present, it has the potential to become a low-cost, precise, and highly available indoor positioning technology.

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“…Similar IBL approaches have been used for indoors as well [18,30,31]. For example, fiducial markers have been utilized in an indoor localization framework; by specifying the 3D locations of such markers with respect to the indoor environment, the transformation between the marker and the camera can be obtained when recognizing it in the image [32][33][34].…”

Section: Literature Reviewmentioning

confidence: 99%

FloorVLoc: A Modular Approach to Floorplan Monocular Localization

2020

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Intelligent vehicles for search and rescue, whose mission is assisting emergency personnel by visually exploring an unfamiliar building, require accurate localization. With GPS not available, and approaches relying on new infrastructure installation, artificial landmarks, or pre-constructed dense 3D maps not feasible, the question is whether there is an approach which can combine ubiquitous prior map information with a monocular camera for accurate positioning. Enter FloorVLoc—Floorplan Vision Vehicle Localization. We provide a means to integrate a monocular camera with a floorplan in a unified and modular fashion so that any monocular visual Simultaneous Localization and Mapping (SLAM) system can be seamlessly incorporated for global positioning. Using a floorplan is especially beneficial since walls are geometrically stable, the memory footprint is low, and prior map information is kept at a minimum. Furthermore, our theoretical analysis of the visual features associated with the walls shows how drift is corrected. To see this approach in action, we developed two full global positioning systems based on the core methodology introduced, operating in both Monte Carlo Localization and linear optimization frameworks. Experimental evaluation of the systems in simulation and a challenging real-world environment demonstrates that FloorVLoc performs with an average error of 0.06 m across 80 m in real-time.

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Drift-Aware Monocular Localization Based on a Pre-Constructed Dense 3D Map in Indoor Environments

Cited by 8 publications

References 39 publications

Drift‐free localisation using prior cross‐source map for indoor low‐light environments

Drift‐free localisation using prior cross‐source map for indoor low‐light environments

A Precise Indoor Visual Positioning Approach Using a Built Image Feature Database and Single User Image from Smartphone Cameras

FloorVLoc: A Modular Approach to Floorplan Monocular Localization

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