Unsupervised Learning of Monocular Depth and Ego-Motion in Outdoor/Indoor Environments

Gao, Ruipeng; Xiao, Xuan; Xing, Weiwei; Li, Chi; Liu, Lei

doi:10.1109/jiot.2022.3151629

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…The majority of man-made buildings typically adhere to the Manhattan world assumption, where walls are parallel or perpendicular to each other and perpendicular to ceilings and floors [ 91 , 92 , 93 ]. The proposed approach is specifically optimized for indoor environments that conform to this assumption, exhibiting enhanced registration performance compared to traditional methods.…”

Section: Discussionmentioning

confidence: 99%

Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System

Park,

Ju,

Nguyen

et al. 2023

Sensors

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The latest advances in mobile platforms, such as robots, have enabled the automatic acquisition of full coverage point cloud data from large areas with terrestrial laser scanning. Despite this progress, the crucial post-processing step of registration, which aligns raw point cloud data from separate local coordinate systems into a unified coordinate system, still relies on manual intervention. To address this practical issue, this study presents an automated point cloud registration approach optimized for a stop-and-go scanning system based on a quadruped walking robot. The proposed approach comprises three main phases: perpendicular constrained wall-plane extraction; coarse registration with plane matching using point-to-point displacement calculation; and fine registration with horizontality constrained iterative closest point (ICP). Experimental results indicate that the proposed method successfully achieved automated registration with an accuracy of 0.044 m and a successful scan rate (SSR) of 100% within a time frame of 424.2 s with 18 sets of scan data acquired from the stop-and-go scanning system in a real-world indoor environment. Furthermore, it surpasses conventional approaches, ensuring reliable registration for point cloud pairs with low overlap in specific indoor environmental conditions.

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Section: Discussionmentioning

confidence: 99%

Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System

Park,

Ju,

Nguyen

et al. 2023

Sensors

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“…1. To achieve homogeneous data fusion between LiDAR's vertex and normal estimation, we implement the Soft Mask Attentional Fusion (SMAF) [3] [13] [14]. For heterogeneous data fusion between LiDAR and IMU, we introduce the Transformer [15] encoder architecture.…”

Section: Introductionmentioning

confidence: 99%

CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation

Sun

Ding

Yoshiyasu

et al. 2022

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Multi-modal fusion of sensors is a commonly used approach to enhance the performance of odometry estimation, which is also a fundamental module for mobile robots. However, the question of how to perform fusion among different modalities in a supervised sensor fusion odometry estimation task? is still one of challenging issues remains. Some simple operations, such as element-wise summation and concatenation, are not capable of assigning adaptive attentional weights to incorporate different modalities efficiently, which make it difficult to achieve competitive odometry results. Recently, the Transformer architecture has shown potential for multi-modal fusion tasks, particularly in the domains of vision with language. In this work, we propose an end-to-end supervised Transformer-based LiDAR-Inertial fusion framework (namely TransFusionOdom) for odometry estimation. The multi-attention fusion module demonstrates different fusion approaches for homogeneous and heterogeneous modalities to address the overfitting problem that can arise from blindly increasing the complexity of the model. Additionally, to interpret the learning process of the Transformer-based multi-modal interactions, a general visualization approach is introduced to illustrate the interactions between modalities. Moreover, exhaustive ablation studies evaluate different multi-modal fusion strategies to verify the performance of the proposed fusion strategy. A synthetic multi-modal dataset is made public to validate the generalization ability of the proposed fusion strategy, which also works for other combinations of different modalities. The quantitative and qualitative odometry evaluations on the KITTI dataset verify the proposed TransFusionOdom could achieve superior performance compared with other related works.

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Self-supervised multi-frame depth estimation with visual-inertial pose transformer and monocular guidance

Wang,

Luo,

Wang

et al. 2024

Information Fusion

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Unsupervised Learning of Monocular Depth and Ego-Motion in Outdoor/Indoor Environments

Cited by 9 publications

References 36 publications

Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System

Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System

CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation

Self-supervised multi-frame depth estimation with visual-inertial pose transformer and monocular guidance

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