Global pose estimation with limited GPS and long range visual odometry

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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Development of Stereo Visual Odometry Based on Photogrammetric Feature Optimization

Remote Sensing ◽

10.3390/rs11010067 ◽

2019 ◽

Vol 11 (1) ◽

pp. 67 ◽

Cited By ~ 4

Author(s):

Sung-Joo Yoon ◽

Taejung Kim

Keyword(s):

Pose Estimation ◽

Automobile Industry ◽

Visual Odometry ◽

Geometric Interpretation ◽

Bundle Adjustment ◽

Processing Technologies ◽

Extraction Step ◽

Feature Optimization ◽

Institute Of Technology ◽

Stereo Visual Odometry

One of the important image processing technologies is visual odometry (VO) technology. VO estimates platform motion through a sequence of images. VO is of interest in the virtual reality (VR) industry as well as the automobile industry because the construction cost is low. In this study, we developed stereo visual odometry (SVO) based on photogrammetric geometric interpretation. The proposed method performed feature optimization and pose estimation through photogrammetric bundle adjustment. After corresponding the point extraction step, the feature optimization was carried out with photogrammetry-based and vision-based optimization. Then, absolute orientation was performed for pose estimation through bundle adjustment. We used ten sequences provided by the Karlsruhe institute of technology and Toyota technological institute (KITTI) community. Through a two-step optimization process, we confirmed that the outliers, which were not removed by conventional outlier filters, were removed. We also were able to confirm the applicability of photogrammetric techniques to stereo visual odometry technology.

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Pose Estimation Based on Monocular Visual Odometry and Lane Detection for Intelligent Vehicles

Lecture Notes in Computer Science - Augmented Reality, Virtual Reality, and Computer Graphics ◽

10.1007/978-3-319-95282-6_40 ◽

2018 ◽

pp. 562-566 ◽

Cited By ~ 4

Author(s):

Juan Galarza ◽

Esteban Pérez ◽

Esteban Serrano ◽

Andrés Tapia ◽

Wilbert G. Aguilar

Keyword(s):

Pose Estimation ◽

Visual Odometry ◽

Intelligent Vehicles ◽

Lane Detection

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PoseGTAC: Graph Transformer Encoder-Decoder with Atrous Convolution for 3D Human Pose Estimation

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/188 ◽

2021 ◽

Author(s):

Yiran Zhu ◽

Xing Xu ◽

Fumin Shen ◽

Yanli Ji ◽

Lianli Gao ◽

...

Keyword(s):

Long Range ◽

Pose Estimation ◽

Contextual Information ◽

Receptive Fields ◽

Human Pose Estimation ◽

Multi Scale ◽

Single Scale ◽

Human Pose ◽

Graph Neural Networks ◽

3D Human Pose Estimation

Graph neural networks (GNNs) have been widely used in the 3D human pose estimation task, since the pose representation of a human body can be naturally modeled by the graph structure. Generally, most of the existing GNN-based models utilize the restricted receptive ﬁelds of ﬁlters and single-scale information, while neglecting the valuable multi-scale contextual information. To tackle this issue, we propose a novel Graph Transformer Encoder-Decoder with Atrous Convolution, named PoseGTAC, to effectively extract multi-scale context and long-range information. In our proposed PoseGTAC model, Graph Atrous Convolution (GAC) and Graph Transformer Layer (GTL), respectively for the extraction of local multi-scale and global long-range information, are combined and stacked in an encoder-decoder structure, where graph pooling and unpooling are adopted for the interaction of multi-scale information from local to global (e.g., part-scale and body-scale). Extensive experiments on the Human3.6M and MPI-INF-3DHP datasets demonstrate that the proposed PoseGTAC model exceeds all previous methods and achieves state-of-the-art performance.

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