MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry

We propose the MAC-VO, a novel learning-based stereo VO that leverages the learned metrics-aware matching uncertainty for dual purposes: selecting keypoint and weighing the residual in pose graph optimization. Compared to traditional geometric methods prioritizing texture-affluent features like edges, our keypoint selector employs the learned uncertainty to filter out the low-quality features based on global inconsistency. In contrast to the learning-based algorithms that model the scale-agnostic diagonal weight matrix for covariance, we design a metrics-aware covariance model to capture the spatial error during keypoint registration and the correlations between different axes. Integrating this covariance model into pose graph optimization enhances the robustness and reliability of pose estimation, particularly in challenging environments with varying illumination, feature density, and motion patterns. On public benchmark datasets, MAC-VO outperforms existing VO algorithms and even some SLAM algorithms in challenging environments. The covariance map also provides valuable information about the reliability of the estimated poses, which can benefit decision-making for autonomous systems.

Imperative Learning: A Self-supervised Neural-Symbolic Learning Framework for Robot Autonomy

Data-driven methods such as reinforcement and imitation learning have achieved remarkable success in robot autonomy. However, their data-centric nature still hinders them from generalizing well to ever-changing environments. Moreover, collecting large datasets for robotic tasks is often impractical and expensive. To overcome these challenges, we introduce a new self-supervised neural-symbolic (NeSy) computational framework, imperative learning (IL), for robot autonomy, leveraging the generalization abilities of symbolic reasoning. The framework of IL consists of three primary components: a neural module, a reasoning engine, and a memory system. We formulate IL as a special bilevel optimization (BLO), which enables reciprocal learning over the three modules. This overcomes the label-intensive obstacles associated with data-driven approaches and takes advantage of symbolic reasoning concerning logical reasoning, physical principles, geometric analysis, etc. We discuss several optimization techniques for IL and verify their effectiveness in five distinct robot autonomy tasks including path planning, rule induction, optimal control, visual odometry, and multi-robot routing. Through various experiments, we show that IL can significantly enhance robot autonomy capabilities and we anticipate that it will catalyze further research across diverse domains.

AirIMU: Learning Uncertainty Propagation for Inertial Odometry

Accurate uncertainty estimation for inertial odometry is the foundation to achieve optimal fusion in multi-sensor systems, such as visual or LiDAR inertial odometry. Prior studies often simplify the assumptions regarding the uncertainty of inertial measurements, presuming fixed covariance parameters and empirical IMU sensor models. However, the inherent physical limitations and non-linear characteristics of sensors are difficult to capture. Moreover, uncertainty may fluctuate based on sensor rates and motion modalities, leading to variations across different IMUs. To address these challenges, we formulate a learning-based method that not only encapsulate the non-linearities inherent to IMUs but also ensure the accurate propagation of covariance in a data-driven manner. We extend the PyPose library to enable differentiable batched IMU integration with covariance propagation on manifolds, leading to significant runtime speedup. To demonstrate our method's adaptability, we evaluate it on several benchmarks as well as a large-scale helicopter dataset spanning over 262 kilometers. The drift rate of the inertial odometry on these datasets is reduced by a factor of between 2.2 and 4 times. Our method lays the groundwork for advanced developments in inertial odometry.

PyPose v0.6: The Imperative Programming Interface for Robotics

PyPose is an open-source library for robot learning. It combines a learning-based approach with physics-based optimization, which enables seamless end-to-end robot learning. It has been used in many tasks due to its meticulously designed application programming interface (API) and efficient implementation. From its initial launch in early 2022, PyPose has experienced significant enhancements, incorporating a wide variety of new features into its platform. To satisfy the growing demand for understanding and utilizing the library and reduce the learning curve of new users, we present the fundamental design principle of the imperative programming interface, and showcase the flexible usage of diverse functionalities and modules using an extremely simple Dubins car example. We also demonstrate that the PyPose can be easily used to navigate a real quadruped robot with a few lines of code.

SubT-MRS: A Subterranean, Multi-Robot, Multi-Spectral and Multi-Degraded Dataset for Robust SLAM

In recent years, significant progress has been made in the field of simultaneous localization and mapping (SLAM) research. However, current state-of-the-art solutions still struggle with limited accuracy and robustness in real-world applications. One major reason is the lack of datasets that fully capture the conditions faced by robots in the wild. To address this problem, we present SubT-MRS, an extremely challenging real-world dataset designed to push the limits of SLAM and perception algorithms. SubT-MRS is a multi-modal, multi-robot dataset collected mainly from subterranean environments having multi-degraded conditions including structureless corridors, varying lighting conditions, and perceptual obscurants such as smoke and dust. Furthermore, the dataset packages information from a diverse range of time-synchronized sensors, including LiDAR, visual cameras, thermal cameras, and IMUs captured using varied vehicular motions like aerial, legged, and wheeled, to support research in sensor fusion, which is essential for achieving accurate and robust robotic perception in complex environments. To evaluate the accuracy of SLAM systems, we also provide a dense 3D model with sub-centimeter-level accuracy, as well as accurate 6DoF ground truth. Our benchmarking approach includes several state-of-the-art methods to demonstrate the challenges our datasets introduce, particularly in the case of multi-degraded environments.

PyPose: A Library for Robot Learning with Physics-based Optimization

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and the reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization techniques. Our design goal for PyPose is to make it user-friendly, efficient, and interpretable with a tidy and well-organized architecture. Using an imperative style interface, it can be easily integrated into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and $2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments show that PyPose achieves 3-20$\times$ speedup in computation compared to state-of-the-art libraries. To boost future research, we provide concrete examples across several fields of robotics, including SLAM, inertial navigation, planning, and control.

AirObject: A Temporally Evolving Graph Embedding for Object Identification

Object encoding and identification are vital for robotic tasks such as autonomous exploration, semantic scene understanding, and re-localization. Previous approaches have attempted to either track objects or generate descriptors for object identification. However, such systems are limited to a "fixed" partial object representation from a single viewpoint. In a robot exploration setup, there is a requirement for a temporally "evolving" global object representation built as the robot observes the object from multiple viewpoints. Furthermore, given the vast distribution of unknown novel objects in the real world, the object identification process must be class-agnostic. In this context, we propose a novel temporal 3D object encoding approach, dubbed AirObject, to obtain global keypoint graph-based embeddings of objects. Specifically, the global 3D object embeddings are generated using a temporal convolutional network across structural information of multiple frames obtained from a graph attention-based encoding method. We demonstrate that AirObject achieves the state-of-the-art performance for video object identification and is robust to severe occlusion, perceptual aliasing, viewpoint shift, deformation, and scale transform, outperforming the state-of-the-art single-frame and sequential descriptors. To the best of our knowledge, AirObject is one of the first temporal object encoding methods.

Unsupervised Online Learning for Robotic Interestingness with Visual Memory

Autonomous robots frequently need to detect "interesting" scenes to decide on further exploration, or to decide which data to share for cooperation. These scenarios often require fast deployment with little or no training data. Prior work considers "interestingness" based on data from the same distribution. Instead, we propose to develop a method that automatically adapts online to the environment to report interesting scenes quickly. To address this problem, we develop a novel translation-invariant visual memory and design a three-stage architecture for long-term, short-term, and online learning, which enables the system to learn human-like experience, environmental knowledge, and online adaption, respectively. With this system, we achieve an average of 20% higher accuracy than the state-of-the-art unsupervised methods in a subterranean tunnel environment. We show comparable performance to supervised methods for robot exploration scenarios showing the efficacy of our approach. We expect that the presented method will play an important role in the robotic interestingness recognition exploration tasks.

AirDOS: Dynamic SLAM benefits from Articulated Objects

Dynamic Object-aware SLAM (DOS) exploits object-level information to enable robust motion estimation in dynamic environments. It has attracted increasing attention with the recent success of learning-based models. Existing methods mainly focus on identifying and excluding dynamic objects from the optimization. In this paper, we show that feature-based visual SLAM systems can also benefit from the presence of dynamic articulated objects by taking advantage of two observations: (1) The 3D structure of an articulated object remains consistent over time; (2) The points on the same object follow the same motion. In particular, we present AirDOS, a dynamic object-aware system that introduces rigidity and motion constraints to model articulated objects. By jointly optimizing the camera pose, object motion, and the object 3D structure, we can rectify the camera pose estimation, preventing tracking loss, and generate 4D spatio-temporal maps for both dynamic objects and static scenes. Experiments show that our algorithm improves the robustness of visual SLAM algorithms in challenging crowded urban environments. To the best of our knowledge, AirDOS is the first dynamic object-aware SLAM system demonstrating that camera pose estimation can be improved by incorporating dynamic articulated objects.

Lifelong Graph Learning

Graph neural networks are powerful models for many graph-structured tasks. In this paper, we aim to solve the problem of lifelong learning for graph neural networks. One of the main challenges is the effect of "catastrophic forgetting" for continuously learning a sequence of tasks, as the nodes can only be present to the model once. Moreover, the number of nodes changes dynamically in lifelong learning and this makes many graph models and sampling strategies inapplicable. To solve these problems, we construct a new graph topology, called the feature graph. It takes features as new nodes and turns nodes into independent graphs. This successfully converts the original problem of node classification to graph classification. In this way, the increasing nodes in lifelong learning can be regarded as increasing training samples, which makes lifelong learning easier. We demonstrate that the feature graph achieves much higher accuracy than the state-of-the-art methods in both data-incremental and class-incremental tasks. We expect that the feature graph will have broad potential applications for graph-structured tasks in lifelong learning.