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 .

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.

Autonomous mining machines must maintain an accurate model of the terrain they work in to support material movement planning and enable autonomous excavation. Terrain mapping is particularly challenging as these machines continuously modify the terrain they work in, often in the presence of other machines that simultaneously transform the same environment. This paper focuses on developing a capability to accurately create and maintain terrain maps that quickly reflect changes in the environment. The proposed approach extends the well-known height grid terrain representation to include a hidden Markov model per cell, allowing for confidence-based mapping of the constantly changing terrain that is robust to sensor noise. Hidden Markov models provide a probabilistic framework for handling uncertain observations and fusing sensor information to estimate the terrain height. The performance of the proposed mapping approach is evaluated on two field datasets: (i) an instrumented hydraulic excavator loading blasted ore into trucks, and (ii) a track-type tractor stacking material in a coal stockyard. The evaluation demonstrates the ability to accurately update the terrain estimate using the machine’s pose and point cloud data captured from machine-mounted LiDARs. The significance of this work is its capacity to provide real-time and accurate maps of changing terrain to support autonomous excavation.

Autonomous agents require the capability to identify dynamic objects in their environment for safe planning and navigation. Incomplete and erroneous dynamic detections jeopardize the agent’s ability to accomplish its task successfully. Dynamic detection is a challenging problem due to the numerous sources of uncertainty inherent in interpreting sensor measurements and the wide variety of applications, which often lead to use-case-tailored solutions. We propose a robust approach to segmenting moving objects in point cloud data. The foundation of the approach lies in describing each voxel using a hidden Markov model (HMM) to use a change-point detection approach to identify dynamic voxels. The proposed approach is evaluated on benchmark datasets using handheld, robot-mounted, and vehicle-mounted LiDARs, each with varying sensor characteristics. We consistently achieve performance that is better than or on par with state-of-the-art results across all scenarios, with strong generalized performance using the same algorithm configuration. Our analysis reveals inconsistencies in benchmarking metrics and ground-truth labelling methodologies for the various public domain datasets, making meaningful comparisons between moving object segmentation (MOS) algorithms challenging. This underscores the need for a standardized definition of moving points and corresponding benchmark frameworks that enable fair and accurate performance evaluations across algorithmic approaches. The proposed approach is open-sourced at https://github.com/vb44/HMM-MOS .

Autonomous agents require the capability to identify dynamic objects in their environment for safe planning and navigation. Incomplete and erroneous dynamic detections jeopardize the agent's ability to accomplish its task. Dynamic detection is a challenging problem due to the numerous sources of uncertainty inherent in the problem's inputs and the wide variety of applications, which often lead to use-case-tailored solutions. We propose a robust learning-free approach to segment moving objects in point cloud data. The foundation of the approach lies in modelling each voxel using a hidden Markov model (HMM), and probabilistically integrating beliefs into a map using an HMM filter. The proposed approach is tested on benchmark datasets and consistently performs better than or as well as state-of-the-art methods with strong generalized performance across sensor characteristics and environments. The approach is open-sourced at https://github.com/vb44/HMM-MOS.

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.