GEN-SLAM: Generative Modeling for Monocular Simultaneous Localization and Mapping

Chakravarty, Punarjay; Narayanan, Praveen; Roussel, Tom

doi:10.48550/arxiv.1902.02086

“…CodeSLAM [200] proposes a depth map from single image, which can be optimised efficiently jointly with pose variables. Mono-stixels [201] uses the depth, motion and semantic information in dynamic scene to estimate depth.…”

Section: D-sismentioning

confidence: 99%

A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

Huang,

Zhao,

Luo

et al. 2021

JGPS

1

0

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Simultaneous Localization and Mapping (SLAM) achieves the purpose of simultaneous positioning and map construction based on self-perception. The paper makes an overview in SLAM including Lidar SLAM, visual SLAM, and their fusion. For Lidar or visual SLAM, the survey illustrates the basic type and product of sensors, open source system in sort and history, deep learning embedded, the challenge and future. Additionally, visual inertial odometry is supplemented. For Lidar and visual fused SLAM, the paper highlights the multi-sensors calibration, the fusion in hardware, data, task layer. The open question and forward thinking end the paper. The contributions of this paper can be summarized as follows: the paper provides a high quality and full-scale overview in SLAM. It's very friendly for new researchers to hold the development of SLAM and learn it very obviously. Also, the paper can be considered as dictionary for experienced researchers to search and find new interested orientation.

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“…GeoNet [199] is a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos. CodeSLAM [200] proposes a depth map from single image, which can be optimised efficiently jointly with pose variables. Mono-stixels [201] uses the depth, motion and semantic information in dynamic scene to estimate depth.…”

Section: Deep Learning With Visual Slammentioning

confidence: 99%

A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

Huang,

Zhao,

Luo

et al. 2021

JGPS

3

0

View full text Add to dashboard Cite

Simultaneous Localization and Mapping (SLAM) achieves the purpose of simultaneous positioning and map construction based on self-perception. The paper makes an overview in SLAM including Lidar SLAM, visual SLAM, and their fusion. For Lidar or visual SLAM, the survey illustrates the basic type and product of sensors, open source system in sort and history, deep learning embedded, the challenge and future. Additionally, visual inertial odometry is supplemented. For Lidar and visual fused SLAM, the paper highlights the multi-sensors calibration, the fusion in hardware, data, task layer. The open question and an envision in 6G wieless networks with SLAM end the paper. The contributions of this paper can be summarized as follows: the paper provides a high quality and full-scale overview in SLAM. It's very friendly for new researchers to hold the development of SLAM and learn it very obviously. Also, the paper can be considered as dictionary for experienced researchers to search and find new interested orientation.

show abstract

“…CodeSLAM [184] proposes a depth map from single image, which can be optimised efficiently jointly with pose variables. GEN-SLAM [185] outputs the dense map with the aid of conventional geometric SLAM and the topological constraint in monocular. [186] proposes a training objective that is invariant to changes in depth range and scale.…”

Section: ) Deep Learning With Visual Slammentioning

confidence: 99%

A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

Huang,

Zhao,

Liu

2019

Preprint

View full text Add to dashboard Cite

Simultaneous Localization and Mapping (SLAM) achieves the purpose of simultaneous positioning and map construction based on self-perception. The paper makes an overview in SLAM including Lidar SLAM, visual SLAM, and their fusion. For Lidar or visual SLAM, the survey illustrates the basic type and product of sensors, open source system in sort and history, deep learning embedded, the challenge and future. Additionally, visual inertial odometry is supplemented. For Lidar and visual fused SLAM, the paper highlights the multi-sensors calibration, the fusion in hardware, data, task layer. The open question and forward thinking end the paper. The contributions of this paper can be summarized as follows: the paper provides a high quality and full-scale overview in SLAM. It's very friendly for new researchers to hold the development of SLAM and learn it very obviously. Also, the paper can be considered as dictionary for experienced researchers to search and find new interested orientation.

show abstract

GEN-SLAM: Generative Modeling for Monocular Simultaneous Localization and Mapping

Cited by 3 publications

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A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

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