Low-latency perception in off-road dynamical low visibility environments

Visual object grasping can be complex when dealing with different shapes, points of view, and environments since the robotic manipulator must estimate the most feasible place to grasp. This work proposes a new selective grasping system using only point clouds of objects. For the selection of the object of interest, a deep learning network for object classification is proposed, named Point Encoder Convolution (PEC). The network is trained with a dataset obtained in a realistic simulator and uses an autoencoder with 1D convolution. The developed grasping algorithm used in the system uses geometry primitives and lateral curvatures to estimate the best region to grasp without previously knowing the object’s point cloud. Experimental results show a success ratio of 94% for a dataset with five classes, and the proposed visual selective grasping system can be executed in around 0.004 s, suitable for tasks that require a low execution time or use low-cost hardware.

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Low-latency perception in off-road dynamical low visibility environments

Cited by 4 publications

References 19 publications

Influence of visual environments on struck-by hazards for construction equipment operators through virtual eye-tracking

Influence of visual environments on struck-by hazards for construction equipment operators through virtual eye-tracking

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A Fast 6DOF Visual Selective Grasping System Using Point Clouds

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