Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data

Phillips, Tyson Govan; D’Adamo, Timothy; McĂree, P. R.

doi:10.3390/s21196473

Cited by 5 publications

(11 citation statements)

References 20 publications

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“…This usually requires a preprocessing step, often individually tailored to the application. It is for this reason that we previously pursued reward-based solutions that do not require segmenting of complex point cloud measurements [ 2 ].…”

Section: The Challenges Of Estimating Object Pose In Point Cloud Datamentioning

confidence: 99%

“…The Maximum Sum of Evidence method introduced by Phillips et al [ 2 ] provides a robust probabilistic approach for model-to-point cloud registration, capable of determining the 6-DOF pose in noisy and occluded environments with no initial alignment. The method is robust, but computationally expensive due to the need to perform raycasting operations across multiple alternative hypotheses.…”

Section: Related Workmentioning

confidence: 99%

“…PLuM is an approximation of the Bayesian-based pose estimation algorithm called the Maximum Sum of Evidence (MSoE) method proposed by the authors in [ 2 ]. The reward metric of MSoE is based in the range space (i.e., comparing raw range measurement observations against raycast measurements under the assumed hypothesis).…”

Section: Problem Formulationmentioning

confidence: 99%

“…This paper presents a solution that is straightforward to implement, does not require configuration, and meets the challenges of accuracy, robustness, and timeliness for a broad class of scenarios. The method is based on similar concepts presented in our previous work [ 1 , 2 ]. The issues of reliability, accuracy, and robustness are addressed by utilising reward-based metrics.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: The Challenges Of Estimating Object Pose In Point Cloud Datamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Bhandari

Phillips

McĂree

2023

Sensors

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“…Refs. [ 35 , 36 ] show how pose estimates of known objects can be obtained from only LiDAR scans by finding the pose that is most likely among the set of all possible poses.…”

Section: Introductionmentioning

confidence: 99%

LiDAR-Stabilised GNSS-IMU Platform Pose Tracking

D’Adamo

Phillips

McĂree

2022

Sensors

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The requirement to estimate the six degree-of-freedom pose of a moving platform frequently arises in automation applications. It is common to estimate platform pose by the fusion of global navigation satellite systems (GNSS) measurements and translational acceleration and rotational rate measurements from an inertial measurement unit (IMU). This paper considers a specific situation where two GNSS receivers and one IMU are used and gives the full formulation of a Kalman filter-based estimator to do this. A limitation in using this sensor set is the difficulty of obtaining accurate estimates of the degree of freedom corresponding to rotation about the line passing through the two GNSS receiver antenna centres. The GNSS-aided IMU formulation is extended to incorporate LiDAR measurements in both known and unknown environments to stabilise this degree of freedom. The performance of the pose estimator is established by comparing expected LiDAR range measurements with actual range measurements. Distributions of the terrain point-to-model error are shown to improve from 0.27m mean error to 0.06m when the GNSS-aided IMU estimator is augmented with LiDAR measurements. This precision is marginally degraded to 0.14m when the pose estimator is operated in an a prior unknown environment.

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Minimal configuration point cloud odometry and mapping

Bhandari,

Phillips,

McAree

2024

The International Journal of Robotics Research

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Simultaneous Localization and Mapping (SLAM) refers to the common requirement for autonomous platforms to estimate their pose and map their surroundings. There are many robust and real-time methods available for solving the SLAM problem. Most are divided into a front-end, which performs incremental pose estimation, and a back-end, which smooths and corrects the results. A low-drift front-end odometry solution is needed for robust and accurate back-end performance. Front-end methods employ various techniques, such as point cloud-to-point cloud (PC2PC) registration, key feature extraction and matching, and deep learning-based approaches. The front-end algorithms have become increasingly complex in the search for low-drift solutions and many now have large configuration parameter sets. It is desirable that the front-end algorithm should be inherently robust so that it does not need to be tuned by several, perhaps many, configuration parameters to achieve low drift in various environments. To address this issue, we propose Simple Mapping and Localization Estimation (SiMpLE), a front-end LiDAR-only odometry method that requires five low-sensitivity configurable parameters. SiMpLE is a scan-to-map point cloud registration algorithm that is straightforward to understand, configure, and implement. We evaluate SiMpLE using the KITTI, MulRan, UrbanNav, and a dataset created at the University of Queensland. SiMpLE performs among the top-ranked algorithms in the KITTI dataset and outperformed all prominent open-source approaches in the MulRan dataset whilst having the smallest configuration set. The UQ dataset also demonstrated accurate odometry with low-density point clouds using Velodyne VLP-16 and Livox Horizon LiDARs. SiMpLE is a front-end odometry solution that can be integrated with other sensing modalities and pose graph-based back-end methods for increased accuracy and long-term mapping. The lightweight and portable code for SiMpLE is available at: https://github.com/vb44/SiMpLE .

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Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data

Cited by 5 publications

References 20 publications

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

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

LiDAR-Stabilised GNSS-IMU Platform Pose Tracking

Minimal configuration point cloud odometry and mapping

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