Assistive Delivery Robot Application for Real-World Postal Services

Lee, Daegyu; Kang, Gyuree; Kim, Boseong; Shim, David Hyunchul

doi:10.1109/access.2021.3120618

Cited by 41 publications

(11 citation statements)

References 53 publications

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“…In existing SLAM algorithms, several submap generation methods have been proposed for global state optimization, such as the radius‐based submap generation method (Lee et al, 2021) or the method using a specific number of history keyframes (Shan et al, 2020). The submap generation results for each technique are presented in Figure 6.…”

Section: Methodsmentioning

confidence: 99%

An autonomous UAV system based on adaptive LiDAR inertial odometry for practical exploration in complex environments

Kim,

Azhari,

Park

et al. 2024

Journal of Field Robotics

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Unmanned aerial vehicles (UAVs) offer many advantages over ground vehicles, including quadruped robots, based on high maneuverability when performing exploration in complex and unknown environments. However, due to their limited computational capability, UAVs require lightweight but accurate state estimation algorithms for reliable exploration. In this paper, we propose a segmented map‐based exploration system based on light detection and ranging (LiDAR)‐based state estimation for UAVs. The proposed system includes capabilities such as exploration, obstacle avoidance, and object detection with localization using three‐dimensional (3D) dense maps generated by tightly coupled LiDAR Inertial Odometry (LIO). Our proposed system is a hybrid system that can switch between guided and exploration modes, making it practical for search and rescue missions in disaster scenarios. The proposed LIO algorithm adapts to its surroundings, allowing for fast and accurate state estimation in complex environments. The proposed exploration algorithm is designed to cover specific regions in the 3D dense map generated by the proposed LIO, with the UAV determining if map points are included within the coverage area. We tested the proposed system in both simulation and real‐world environments and validated that the proposed system outperforms state‐of‐the‐art algorithms in various aspects such as localization accuracy and exploration efficiency in complex environments.

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Section: Methodsmentioning

confidence: 99%

An autonomous UAV system based on adaptive LiDAR inertial odometry for practical exploration in complex environments

Kim,

Azhari,

Park

et al. 2024

Journal of Field Robotics

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“…Many emerging applications of robots have the potential of assisting humans in everyday life tasks or automating jobs in the future [1]. Examples include social robots offering assistance at receptions [2], in hospitality sectors [3] or at home [4]; navigation guidance in public spaces [5] or personal care [6]; and object delivery [7]. In such situations, robots should automatically understand the human intention to interact well before the interaction starts to be more proactive and offer relevant services.…”

Section: Introductionmentioning

confidence: 99%

Self-supervised prediction of the intention to interact with a service robot

Abbate,

Giusti,

Schmuck

et al. 2024

Robotics and Autonomous Systems

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“…W ITH the rapidly expanding use of mobile robots in areas like agriculture [1] and delivery [2], it is critical that such robots are able to effectively understand their environments in order to navigate and successfully complete their tasks. Traversability estimation, which involves determining where in the environment a robot can travel, is one common method for solving this problem in unstructured environments [3].…”

Section: Introductionmentioning

confidence: 99%

W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics

Schreiber,

Sivakumar,

et al. 2024

IEEE Robot. Autom. Lett.

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Successful deployment of mobile robots in unstructured domains requires an understanding of the environment and terrain to avoid hazardous areas, getting stuck, and colliding with obstacles. Traversability estimation-which predicts where in the environment a robot can travel-is one prominent approach that tackles this problem. Existing geometric methods may ignore important semantic considerations, while semantic segmentation approaches involve a tedious labeling process. Recent self-supervised methods reduce labeling tedium, but require additional data or models and tend to struggle to explicitly label untraversable areas. To address these limitations, we introduce a weakly-supervised method for relative traversability estimation. Our method involves manually annotating the relative traversability of a small number of point pairs, which significantly reduces labeling effort compared to traditional segmentation-based methods and avoids the limitations of selfsupervised methods. We further improve the performance of our method through a novel cross-image labeling strategy and loss function. We demonstrate the viability and performance of our method through deployment on a mobile robot in outdoor environments. Code is available at: https://github.com/ andreschreiber/W-RIZZ.

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Assistive Delivery Robot Application for Real-World Postal Services

Cited by 41 publications

References 53 publications

An autonomous UAV system based on adaptive LiDAR inertial odometry for practical exploration in complex environments

An autonomous UAV system based on adaptive LiDAR inertial odometry for practical exploration in complex environments

Self-supervised prediction of the intention to interact with a service robot

W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics

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