Visual Positioning in Indoor Environments Using RGB-D Images and Improved Vector of Local Aggregated Descriptors

Zhang, Longyu; Xia, Hao; Liu, Qingjun; Wei, Chunyang; Fu, Dong; Qiao, Yunfeng

doi:10.3390/ijgi10040195

Cited by 5 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quality of geo-labeled images is the core of these methods, including shape, color, and texture [27]. Zhang [28] proposed the improved vector of local aggregated descriptors to improve the retrieval accuracy under different lighting conditions. Most of the conventional methods retrieve images based on local descriptor matching and reorder them through elaborate spatial verification [29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Indoor Passive Visual Positioning by CNN-Based Pedestrian Detection

Chen

et al. 2022

Micromachines

View full text Add to dashboard Cite

Indoor positioning applications are developing at a rapid pace; active visual positioning is one method that is applicable to mobile platforms. Other methods include Wi-Fi, CSI, and PDR approaches; however, their positioning accuracy usually cannot achieve the positioning performance of the active visual method. Active visual users, however, must take a photo to obtain location information, raising confidentiality and privacy issues. To address these concerns, we propose a solution for passive visual positioning based on pedestrian detection and projection transformation. This method consists of three steps: pretreatment, pedestrian detection, and pose estimation. Pretreatment includes camera calibration and camera installation. In pedestrian detection, features are extracted by deep convolutional neural networks using neighboring frame detection results and the map information as the region of interest attention model (RIAM). Pose estimation computes accurate localization results through projection transformation (PT). This system relies on security cameras installed in non-private areas so that pedestrians do not have to take photos. Experiments were conducted in a hall about 100 square meters in size, with 41 test-points for the localization experiment. The results show that the positioning error was 0.48 m (RMSE) and the 90% error was 0.73 m. Therefore, the proposed passive visual method delivers high positioning performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Indoor Passive Visual Positioning by CNN-Based Pedestrian Detection

Chen

et al. 2022

Micromachines

View full text Add to dashboard Cite

show abstract

“…Following the creation of an offline image database, an efficient image retrieval method is required in order to acquire database images that are visually comparable to the query image. Generally, traditional feature extraction methods or convolutional neural networks are used to extract feature vectors from images, and local feature aggregation descriptors (VLAD) [15], the Fisher Vector, and BoW [9,10] are used to describe vectors to achieve image retrieval. To some degree, the preceding approaches may meet the requirements of indoor positioning.…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval Method Based on Visual Map Pre-Sampling Construction in Indoor Positioning

Bai

Qin

Zheng

et al. 2023

IJGI

View full text Add to dashboard Cite

In visual indoor positioning systems, the method of constructing a visual map by point-by-point sampling is widely used due to its characteristics of clear static images and simple coordinate calculation. However, too small a sampling interval will cause image redundancy, while too large a sampling interval will lead to the absence of any scene images, which will result in worse positioning efficiency and inferior positioning accuracy. As a result, this paper proposed a visual map construction method based on pre-sampled image features matching, according to the epipolar geometry of adjacent position images, to determine the optimal sampling spacing within the constraints and effectively control the database size while ensuring the integrity of the image information. In addition, in order to realize the rapid retrieval of the visual map and reduce the positioning error caused by the time overhead, an image retrieval method based on deep hashing was also designed in this paper. This method used a convolutional neural network to extract image features to construct the semantic similarity structure to guide the generation of hash code. Based on the log-cosh function, this paper proposed a loss function whose function curve was smooth and not affected by outliers, and then integrated it into the deep network to optimize parameters, for fast and accurate image retrieval. Experiments on the FLICKR25K dataset and the visual map proved that the method proposed in this paper could achieve sub-second image retrieval with guaranteed accuracy, thereby demonstrating its promising performance.

show abstract

Monocular vision based on the YOLOv7 and coordinate transformation for vehicles precise positioning

Zhang

et al. 2023

Connection Science

View full text Add to dashboard Cite

Visual Positioning in Indoor Environments Using RGB-D Images and Improved Vector of Local Aggregated Descriptors

Cited by 5 publications

References 33 publications

Indoor Passive Visual Positioning by CNN-Based Pedestrian Detection

Indoor Passive Visual Positioning by CNN-Based Pedestrian Detection

Image Retrieval Method Based on Visual Map Pre-Sampling Construction in Indoor Positioning

Monocular vision based on the YOLOv7 and coordinate transformation for vehicles precise positioning

Contact Info

Product

Resources

About