An Efficient Method for Accurate Pose Estimation and Error Correction of Cuboidal Objects

The proposed system outlined in this paper is a solution to a use case that requires the autonomous picking of cuboidal objects from an organized or unorganized pile with high precision. This paper presents an efficient method for precise pose estimation of cuboid-shaped objects, which aims to reduce errors in target pose in a time-efficient manner. Typical pose estimation methods like global point cloud registrations are prone to minor pose errors for which local registration algorithms are generally used to improve pose accuracy. However, due to the execution time overhead and uncertainty in the error of the final achieved pose, an alternate, linear time approach is proposed for pose error estimation and correction. This paper presents an overview of the solution followed by a detailed description of individual modules of the proposed algorithm.

FA-KPConv: Introducing Euclidean Symmetries to KPConv via Frame Averaging

We present Frame-Averaging Kernel-Point Convolution (FA-KPConv), a neural network architecture built on top of the well-known KPConv, a widely adopted backbone for 3D point cloud analysis. Even though invariance and/or equivariance to Euclidean transformations are required for many common tasks, KPConv-based networks can only approximately achieve such properties when training on large datasets or with significant data augmentations. Using Frame Averaging, we allow to flexibly customize point cloud neural networks built with KPConv layers, by making them exactly invariant and/or equivariant to translations, rotations and/or reflections of the input point clouds. By simply wrapping around an existing KPConv-based network, FA-KPConv embeds geometrical prior knowledge into it while preserving the number of learnable parameters and not compromising any input information. We showcase the benefit of such an introduced bias for point cloud classification and point cloud registration, especially in challenging cases such as scarce training data or randomly rotated test data.

Matching Distance and Geometric Distribution Aided Learning Multiview Point Cloud Registration

Multiview point cloud registration plays a crucial role in robotics, automation, and computer vision fields. This paper concentrates on pose graph construction and motion synchronization within multiview registration. Previous methods for pose graph construction often pruned fully connected graphs or constructed sparse graph using global feature aggregated from local descriptors, which may not consistently yield reliable results. To identify dependable pairs for pose graph construction, we design a network model that extracts information from the matching distance between point cloud pairs. For motion synchronization, we propose another neural network model to calculate the absolute pose in a data-driven manner, rather than optimizing inaccurate handcrafted loss functions. Our model takes into account geometric distribution information and employs a modified attention mechanism to facilitate flexible and reliable feature interaction. Experimental results on diverse indoor and outdoor datasets confirm the effectiveness and generalizability of our approach. The source code is available at https://github.com/Shi-Qi-Li/MDGD.

Enhancing Lidar Point Cloud Sampling via Colorization and Super-Resolution of Lidar Imagery

Recent advancements in lidar technology have led to improved point cloud resolution as well as the generation of 360 degrees, low-resolution images by encoding depth, reflectivity, or near-infrared light within each pixel. These images enable the application of deep learning (DL) approaches, originally developed for RGB images from cameras to lidar-only systems, eliminating other efforts, such as lidar-camera calibration. Compared with conventional RGB images, lidar imagery demonstrates greater robustness in adverse environmental conditions, such as low light and foggy weather. Moreover, the imaging capability addresses the challenges in environments where the geometric information in point clouds may be degraded, such as long corridors, and dense point clouds may be misleading, potentially leading to drift errors. Therefore, this paper proposes a novel framework that leverages DL-based colorization and super-resolution techniques on lidar imagery to extract reliable samples from lidar point clouds for odometry estimation. The enhanced lidar images, enriched with additional information, facilitate improved keypoint detection, which is subsequently employed for more effective point cloud downsampling. The proposed method enhances point cloud registration accuracy and mitigates mismatches arising from insufficient geometric information or misleading extra points. Experimental results indicate that our approach surpasses previous methods, achieving lower translation and rotation errors while using fewer points.

Multiview Point Cloud Registration via Optimization in an Autoencoder Latent Space

Point cloud rigid registration is a fundamental problem in 3D computer vision. In the multiview case, we aim to find a set of 6D poses to align a set of objects. Methods based on pairwise registration rely on a subsequent synchronization algorithm, which makes them poorly scalable with the number of views. Generative approaches overcome this limitation, but are based on Gaussian Mixture Models and use an Expectation-Maximization algorithm. Hence, they are not well suited to handle large transformations. Moreover, most existing methods cannot handle high levels of degradations. In this paper, we introduce POLAR (POint cloud LAtent Registration), a multiview registration method able to efficiently deal with a large number of views, while being robust to a high level of degradations and large initial angles. To achieve this, we transpose the registration problem into the latent space of a pretrained autoencoder, design a loss taking degradations into account, and develop an efficient multistart optimization strategy. Our proposed method significantly outperforms state-of-the-art approaches on synthetic and real data. POLAR is available at github.com/pypolar/polar or as a standalone package which can be installed with pip install polaregistration.

Tightly Coupled Range Inertial Odometry and Mapping with Exact Point Cloud Downsampling

In this work, to facilitate the real-time processing of multi-scan registration error minimization on factor graphs, we devise a point cloud downsampling algorithm based on coreset extraction. This algorithm extracts a subset of the residuals of input points such that the subset yields exactly the same quadratic error function as that of the original set for a given pose. This enables a significant reduction in the number of residuals to be evaluated without approximation errors at the sampling point. Using this algorithm, we devise a complete SLAM framework that consists of odometry estimation based on sliding window optimization and global trajectory optimization based on registration error minimization over the entire map, both of which can run in real time on a standard CPU. The experimental results demonstrate that the proposed framework outperforms state-of-the-art CPU-based SLAM frameworks without the use of GPU acceleration.

LRBO2: Improved 3D Vision Based Hand-Eye Calibration for Collaborative Robot Arm

Hand-eye calibration is a common problem in the field of collaborative robotics, involving the determination of the transformation matrix between the visual sensor and the robot flange to enable vision-based robotic tasks. However, this process typically requires multiple movements of the robot arm and an external calibration object, making it both time-consuming and inconvenient, especially in scenarios where frequent recalibration is necessary. In this work, we extend our previous method, Look at Robot Base Once (LRBO), which eliminates the need for external calibration objects such as a chessboard. We propose a generic dataset generation approach for point cloud registration, focusing on aligning the robot base point cloud with the scanned data. Furthermore, a more detailed simulation study is conducted involving several different collaborative robot arms, followed by real-world experiments in an industrial setting. Our improved method is simulated and evaluated using a total of 14 robotic arms from 9 different brands, including KUKA, Universal Robots, UFACTORY, and Franka Emika, all of which are widely used in the field of collaborative robotics. Physical experiments demonstrate that our extended approach achieves performance comparable to existing commercial hand-eye calibration solutions, while completing the entire calibration procedure in just a few seconds. In addition, we provide a user-friendly hand-eye calibration solution, with the code publicly available at github.com/leihui6/LRBO2.

DRO: Doppler-Aware Direct Radar Odometry

A renaissance in radar-based sensing for mobile robotic applications is underway. Compared to cameras or lidars, millimetre-wave radars have the ability to `see' through thin walls, vegetation, and adversarial weather conditions such as heavy rain, fog, snow, and dust. In this paper, we propose a novel SE(2) odometry approach for spinning frequency-modulated continuous-wave radars. Our method performs scan-to-local-map registration of the incoming radar data in a direct manner using all the radar intensity information without the need for feature or point cloud extraction. The method performs locally continuous trajectory estimation and accounts for both motion and Doppler distortion of the radar scans. If the radar possesses a specific frequency modulation pattern that makes radial Doppler velocities observable, an additional Doppler-based constraint is formulated to improve the velocity estimate and enable odometry in geometrically feature-deprived scenarios (e.g., featureless tunnels). Our method has been validated on over 250km of on-road data sourced from public datasets (Boreas and MulRan) and collected using our automotive platform. With the aid of a gyroscope, it outperforms state-of-the-art methods and achieves an average relative translation error of 0.26% on the Boreas leaderboard. When using data with the appropriate Doppler-enabling frequency modulation pattern, the translation error is reduced to 0.18% in similar environments. We also benchmarked our algorithm using 1.5 hours of data collected with a mobile robot in off-road environments with various levels of structure to demonstrate its versatility. Our real-time implementation is publicly available: https://github.com/utiasASRL/dro.

FACT: Multinomial Misalignment Classification for Point Cloud Registration

We present FACT, a method for predicting alignment quality (i.e., registration error) of registered lidar point cloud pairs. This is useful e.g. for quality assurance of large, automatically registered 3D models. FACT extracts local features from a registered pair and processes them with a point transformer-based network to predict a misalignment class. We generalize prior work that study binary alignment classification of registration errors, by recasting it as multinomial misalignment classification. To achieve this, we introduce a custom regression-by-classification loss function that combines the cross-entropy and Wasserstein losses, and demonstrate that it outperforms both direct regression and prior binary classification. FACT successfully classifies point-cloud pairs registered with both the classical ICP and GeoTransformer, while other choices, such as standard point-cloud-quality metrics and registration residuals are shown to be poor choices for predicting misalignment. On a synthetically perturbed point-cloud task introduced by the CorAl method, we show that FACT achieves substantially better performance than CorAl. Finally, we demonstrate how FACT can assist experts in correcting misaligned point-cloud maps. Our code is available at https://github.com/LudvigDillen/FACT_for_PCMC.

A Pointcloud Registration Framework for Relocalization in Subterranean Environments

Relocalization, the process of re-establishing a robot's position within an environment, is crucial for ensuring accurate navigation and task execution when external positioning information, such as GPS, is unavailable or has been lost. Subterranean environments present significant challenges for relocalization due to limited external positioning information, poor lighting that affects camera localization, irregular and often non-distinct surfaces, and dust, which can introduce noise and occlusion in sensor data. In this work, we propose a robust, computationally friendly framework for relocalization through point cloud registration utilizing a prior point cloud map. The framework employs Intrinsic Shape Signatures (ISS) to select feature points in both the target and prior point clouds. The Fast Point Feature Histogram (FPFH) algorithm is utilized to create descriptors for these feature points, and matching these descriptors yields correspondences between the point clouds. A 3D transformation is estimated using the matched points, which initializes a Normal Distribution Transform (NDT) registration. The transformation result from NDT is further refined using the Iterative Closest Point (ICP) registration algorithm. This framework enhances registration accuracy even in challenging conditions, such as dust interference and significant initial transformations between the target and source, making it suitable for autonomous robots operating in underground mines and tunnels. This framework was validated with experiments in simulated and real-world mine datasets, demonstrating its potential for improving relocalization.