Memory-Augmented Point Cloud Registration Network for Bucket Pose Estimation of the Intelligent Mining Excavator

Cui, Yunhao; An, Yi; Sun, Wei; Hu, Huosheng; Song, Xueguan

doi:10.1109/tim.2022.3149331

Cited by 13 publications

(5 citation statements)

References 26 publications

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“…A large area of research is dedicated to body pose estimation dealing with both rigid and deformable models of humans [30], with an application in identifying pedestrians from autonomous vehicles for safer navigation. Numerous use-cases also arise in the automation of mining and agricultural equipment, with the use of object pose estimation for end-effector tracking [31], and identifying known geometries in the workspace from haul trucks [32] to peach trees [33].…”

Section: Problem Overview and Applicationsmentioning

confidence: 99%

Novel Approaches for Point Cloud Analysis with Evidential Methods: A Multifaceted Approach to Object Pose Estimation, Point Cloud Odometry, and Sensor Registration

Bhandari,

Phillips,

McAree

2024

Point Cloud Generation and Its Applications [Working Title]

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Autonomous agents must understand their environment to make decisions. Perception systems often interpret point cloud measurements to extract beliefs about their surroundings. A common strategy is to seek beliefs that are least likely to be false, commonly known as cost-based approaches. These metrics have limitations in practical applications, such as in the presence of noisy measurements, dynamic objects, and debris. Modern solutions integrate additional stages such as segmentation to counteract these limitations, thereby increasing the complexity of the algorithms while being internally flawed. An alternative strategy is to extract beliefs that are best supported by the data. We call these evidence-based methods. This difference allows for robustness to the limitations of using cost-based methods without needing complex additional stages. Essential perception tasks such as object pose estimation, point cloud odometry, and sensor registration are solved using evidence-based methods. The demonstrated approaches are simple, require minimum configuration and tuning, and circumvents the need for additional processing stages.

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Section: Problem Overview and Applicationsmentioning

confidence: 99%

Novel Approaches for Point Cloud Analysis with Evidential Methods: A Multifaceted Approach to Object Pose Estimation, Point Cloud Odometry, and Sensor Registration

Bhandari,

Phillips,

McAree

2024

Point Cloud Generation and Its Applications [Working Title]

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“…In 3D scanning, the key challenge is the registration of partial point clouds [18], [19]. Its goal is to find the optimal transformations that align the input partial point clouds.…”

Section: B 3d Shape Rigid Registrationmentioning

confidence: 99%

AlignBodyNet: Deep Learning-Based Alignment of Non-Overlapping Partial Body Point Clouds From a Single Depth Camera

Munteanu

2023

IEEE Trans. Instrum. Meas.

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This paper proposes a novel deep learning framework to generate omnidirectional 3D point clouds of human bodies by registering the front-and back-facing partial scans captured by a single depth camera. Our approach does not require calibration-assisting devices, canonical postures, nor does it make assumptions concerning an initial alignment or correspondences between the partial scans. This is achieved by factoring this challenging problem into (i) building virtual correspondences for partial scans, and (ii) implicitly predicting the rigid transformation between the two partial scans via the predicted virtual correspondences. In this study, we regress the SMPL vertices from the two partial scans for building the virtual correspondences. The main challenges are (i) estimating the body shape and pose under clothing from single partial dressed body point clouds, and (ii) the predicted bodies from front-and back-facing inputs required to be the same. We, thus, propose a novel deep neural network dubbed AlignBodyNet that introduces shape-interrelated features and a shape-constraint loss for resolving this problem. We also provide a simple yet efficient method for generating real-world partial scans from complete models, which fills the gap in the lack of quantitative comparisons based on the real-world data for various studies including partial registration, shape completion, and view synthesis. Experiments based on synthetic and real-world data show that our method achieves state-of-the-art performance in both objective and subjective terms.

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“…The mining industry uses registration in an attempt to automate the stages of the excavator loading cycle. Borthwick demonstrated a method for haul truck estimation and load profiling using stereo vision and the ICP registration algorithm [ 23 ], whereas Cui et al [ 24 ] presented a registration network for excavator bucket pose estimation. Phillips et al demonstrated a method for both haul truck and dipper pose estimation using a LiDAR sensor and the Maximum Sum of Evidence method [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

Real-Time 6-DOF Pose Estimation of Known Geometries in Point Cloud Data

Bhandari

Phillips

McĂree

2023

Sensors

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The task of tracking the pose of an object with a known geometry from point cloud measurements arises in robot perception. It calls for a solution that is both accurate and robust, and can be computed at a rate that aligns with the needs of a control system that might make decisions based on it. The Iterative Closest Point (ICP) algorithm is widely used for this purpose, but it is susceptible to failure in practical scenarios. We present a robust and efficient solution for pose-from-point cloud estimation called the Pose Lookup Method (PLuM). PLuM is a probabilistic reward-based objective function that is resilient to measurement uncertainty and clutter. Efficiency is achieved through the use of lookup tables, which substitute complex geometric operations such as raycasting used in earlier solutions. Our results show millimetre accuracy and fast pose estimation in benchmark tests using triangulated geometry models, outperforming state-of-the-art ICP-based methods. These results are extended to field robotics applications, resulting in real-time haul truck pose estimation. By utilising point clouds from a LiDAR fixed to a rope shovel, the PLuM algorithm tracks a haul truck effectively throughout the excavation load cycle at a rate of 20 Hz, matching the sensor frame rate. PLuM is straightforward to implement and provides dependable and timely solutions in demanding environments.

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Memory-Augmented Point Cloud Registration Network for Bucket Pose Estimation of the Intelligent Mining Excavator

Cited by 13 publications

References 26 publications

Novel Approaches for Point Cloud Analysis with Evidential Methods: A Multifaceted Approach to Object Pose Estimation, Point Cloud Odometry, and Sensor Registration

Novel Approaches for Point Cloud Analysis with Evidential Methods: A Multifaceted Approach to Object Pose Estimation, Point Cloud Odometry, and Sensor Registration

AlignBodyNet: Deep Learning-Based Alignment of Non-Overlapping Partial Body Point Clouds From a Single Depth Camera

Real-Time 6-DOF Pose Estimation of Known Geometries in Point Cloud Data

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