Voxel-Grid Based Convex Decomposition of 3D Space for Safe Corridor Generation

Toumieh, Charbel; Lambert, Alain

doi:10.1007/s10846-022-01708-y

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…The source point cloud of the pallet and the scene point cloud containing the pallet are collected using an active binocular vision sensor. The redundant scene information in the scene cloud is removed through the pass-through filtering and voxel mesh downsampling method [ 29 ], and the plane segmentation algorithm is used to obtain the target pallet point cloud. The key points in the target point cloud are extracted by the Intrinsic Shape Signatures (ISS) algorithm [ 30 ].…”

Section: The Proposed Pallet Pose Estimation Methodsmentioning

confidence: 99%

A Point Cloud Data-Driven Pallet Pose Estimation Method Using an Active Binocular Vision Sensor

Shao

Fan

Zhu

et al. 2023

Sensors

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Pallet pose estimation is one of the key technologies for automated fork pickup of driverless industrial trucks. Due to the complex working environment and the enormous amount of data, the existing pose estimation approaches cannot meet the working requirements of intelligent logistics equipment in terms of high accuracy and real time. A point cloud data-driven pallet pose estimation method using an active binocular vision sensor is proposed, which consists of point cloud preprocessing, Adaptive Gaussian Weight-based Fast Point Feature Histogram extraction and point cloud registration. The proposed method overcomes the shortcomings of traditional pose estimation methods, such as poor robustness, time consumption and low accuracy, and realizes the efficient and accurate estimation of pallet pose for driverless industrial trucks. Compared with traditional Fast Point Feature Histogram and Signature of Histogram of Orientation, the experimental results show that the proposed approach is superior to the above two methods, improving the accuracy by over 35% and reducing the feature extraction time by over 30%, thereby verifying the effectiveness and superiority of the proposed method.

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Section: The Proposed Pallet Pose Estimation Methodsmentioning

confidence: 99%

A Point Cloud Data-Driven Pallet Pose Estimation Method Using an Active Binocular Vision Sensor

Shao

Fan

Zhu

et al. 2023

Sensors

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“…Finally, in our previous work [1], we proposed a decentralized and synchronous planning framework that is inspired by [2]. The approach takes into account static obstacles using Safe Corridors (generated from a voxel grid representation [11]). Safe Corridors are then augmented to timeaware Safe Corridors to avoid intra-agent collisions.…”

Section: B Related Workmentioning

confidence: 99%

“…The dynamical model of each agent is the same as presented in [1]. We use a voxel grid representation of the environment, which can be trivially and efficiently generated [11]. Each agent has a voxel grid that is of fixed size and that moves with the agent such that the agent is always at its center.…”

Section: The Plannermentioning

confidence: 99%

“…They are used by many state-of-the-art planning methods to constrain a dynamically feasible trajectory inside them, and thus guarantee safety [16], [14], [1]. Many methods exist in the literature for Safe Corridor generation [17], [18], [19] [20]. The method used for the generation is [19] since it provides the best performance among the state-of-the-art methods for trajectory planning.…”

Section: B Generate a Safe Corridor Around The Global Pathmentioning

confidence: 99%

“…Many methods exist in the literature for Safe Corridor generation [17], [18], [19] [20]. The method used for the generation is [19] since it provides the best performance among the state-of-the-art methods for trajectory planning.…”

Section: B Generate a Safe Corridor Around The Global Pathmentioning

confidence: 99%

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Decentralized Multi-Agent Planning Using Model Predictive Control and Time-Aware Safe Corridors

Toumieh¹,

Lambert²

2022

IEEE Robot. Autom. Lett.

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There are many industrial, commercial and social applications for multi-agent planning for multirotors such as autonomous agriculture, infrastructure inspection and search and rescue. Thus, improving on the state-of-the-art of multiagent planning to make it a viable real-world solution is of great benefit. In this work, we propose a new method for multi-agent planning in a static environment that improves our previous work by making it fully online as well as robust to communication latency. The proposed framework generates a global path and a Safe Corridor to avoid static obstacles in an online fashion (generated offline in our previous work). It then generates a time-aware Safe Corridor which takes into account the future positions of other agents to avoid intra-agent collisions. The time-aware Safe Corridor is given with a local reference trajectory to an MIQP (Mixed-Integer Quadratic Problem)/MPC (Model Predictive Control) solver that outputs a safe and optimal trajectory. The planning frequency is adapted to account for communication delays. The proposed method is fully online, real-time, decentralized, and synchronous. It is compared to 3 recent state-of-the-art methods in simulations. It outperforms all methods in robustness and safety as well as flight time. It also outperforms the only other state-of-the-art latency robust method in computation time.

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