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Found 52 papers, 34 papers with code
Shonan Rotation Averaging: Global Optimality by Surfing SO(p)(n)
Shonan Rotation Averaging is a fast, simple, and elegant rotation averaging algorithm that is guaranteed to recover globally optimal solutions under mild assumptions on the measurement noise.
Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds
We investigate a variation of the 3D registration problem, named multi-model 3D registration.
Indoor and Outdoor 3D Scene Graph Generation via Language-Enabled Spatial Ontologies
This paper proposes an approach to build 3D scene graphs in arbitrary (indoor and outdoor) environments.
Vision-Based Terrain Relative Navigation on High-Altitude Balloon and Sub-Orbital Rocket
Additionally, we evaluate performance on data collected by two cameras inside the capsule of Blue Origin's New Shepard rocket on payload flight NS-23, traveling at speeds up to 880 km/hr, and demonstrate less than 55 meters of average position error.
A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training
Real-world robotics applications demand object pose estimation methods that work reliably across a variety of scenarios.
NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields
We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-time scene reconstruction from monocular images.
Probabilistic Volumetric Fusion for Dense Monocular SLAM
We present a novel method to reconstruct 3D scenes from images by leveraging deep dense monocular SLAM and fast uncertainty propagation.
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
Abstract semantic 3D scene understanding is a problem of critical importance in robotics.
Estimation Contracts for Outlier-Robust Geometric Perception
In particular, we adapt and extend recent results on robust linear regression (applicable to the low-outlier regime with << 50% outliers) and list-decodable regression (applicable to the high-outlier regime with >> 50% outliers) to the setup commonly found in robotics and vision, where (i) variables (e. g., rotations, poses) belong to a non-convex domain, (ii) measurements are vector-valued, and (iii) the number of outliers is not known a priori.
Optimal and Robust Category-level Perception: Object Pose and Shape Estimation from 2D and 3D Semantic Keypoints
We consider an active shape model, where -- for an object category -- we are given a library of potential CAD models describing objects in that category, and we adopt a standard formulation where pose and shape are estimated from 2D or 3D keypoints via non-convex optimization.
Certifiable 3D Object Pose Estimation: Foundations, Learning Models, and Self-Training
Our third contribution is a novel self-supervised training approach that uses our certificate of observable correctness to provide the supervisory signal to C-3PO during training.
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding
Semantic 3D scene understanding is a problem of critical importance in robotics.
Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification
This paper investigates runtime monitoring of perception systems.
Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems
In this survey article, we analyze how resilience is achieved in networks of agents and multi-robot systems that are able to overcome adversity by leveraging system-wide complementarity, diversity, and redundancy - often involving a reconfiguration of robotic capabilities to provide some key ability that was not present in the system a priori.
Certifiably Optimal Outlier-Robust Geometric Perception: Semidefinite Relaxations and Scalable Global Optimization
Our third contribution is to solve the SDP relaxations at an unprecedented scale and accuracy by presenting STRIDE, a solver that blends global descent on the convex SDP with fast local search on the nonconvex POP.
Smooth Mesh Estimation from Depth Data using Non-Smooth Convex Optimization
In this paper, we leapfrog these intermediate representations and build a 3D mesh directly from a depth map and the sparse landmarks triangulated with visual odometry.
Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks
In this work, we present a reinforcement learning framework that leverages high-level hierarchical representations to learn navigation policies.
Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems
This paper presents Kimera-Multi, the first multi-robot system that (i) is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures resulting from perceptual aliasing, (ii) is fully distributed and only relies on local (peer-to-peer) communication to achieve distributed localization and mapping, and (iii) builds a globally consistent metric-semantic 3D mesh model of the environment in real-time, where faces of the mesh are annotated with semantic labels.
An Inexact Projected Gradient Method with Rounding and Lifting by Nonlinear Programming for Solving Rank-One Semidefinite Relaxation of Polynomial Optimization
In particular, we first design a globally convergent inexact projected gradient method (iPGM) for solving the SDP that serves as the backbone of our framework.
Neural Trees for Learning on Graphs
We also prove that the number of parameters needed to achieve an $\epsilon$-approximation of the distribution function is exponential in the treewidth of the input graph, but linear in its size.
Optimal Pose and Shape Estimation for Category-level 3D Object Perception
Our first contribution is to provide the first certifiably optimal solver for pose and shape estimation.
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge.
Dynamic Grasping with a "Soft" Drone: From Theory to Practice
This paper presents the first prototype of a soft drone -- a quadrotor where traditional (i. e., rigid) landing gears are replaced with a soft tendon-actuated gripper to enable aggressive grasping.
Robotics
Self-supervised Geometric Perception
We present self-supervised geometric perception (SGP), the first general framework to learn a feature descriptor for correspondence matching without any ground-truth geometric model labels (e. g., camera poses, rigid transformations).
Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs
This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e. g., objects, rooms, buildings), includes static and dynamic entities and their relations (e. g., a person is in a room at a given time).
Monitoring and Diagnosability of Perception Systems
The resulting temporal diagnostic graphs (i) provide a framework to reason over the consistency of perception outputs -- across modules and over time -- thus enabling fault detection, (ii) allow us to establish formal guarantees on the maximum number of faults that can be uniquely identified in a given perception system, and (iii) enable the design of efficient algorithms for fault identification.
Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping
Our system, dubbed Kimera-Multi, is implemented by a team of robots equipped with visual-inertial sensors, and builds a 3D mesh model of the environment in real-time, where each face of the mesh is annotated with a semantic label (e. g., building, road, objects).
ROBIN: a Graph-Theoretic Approach to Reject Outliers in Robust Estimation using Invariants
We also show that in practice the maximum k-core of the compatibility graph provides an approximation of the maximum clique, while being faster to compute in large problems.
Primal-Dual Mesh Convolutional Neural Networks
Recent works in geometric deep learning have introduced neural networks that allow performing inference tasks on three-dimensional geometric data by defining convolution, and sometimes pooling, operations on triangle meshes.
Shonan Rotation Averaging: Global Optimality by Surfing $SO(p)^n$
Shonan Rotation Averaging is a fast, simple, and elegant rotation averaging algorithm that is guaranteed to recover globally optimal solutions under mild assumptions on the measurement noise.
Outlier-Robust Estimation: Hardness, Minimally Tuned Algorithms, and Applications
We extend ADAPT and GNC to the case where the user does not have prior knowledge of the inlier-noise statistics (or the statistics may vary over time) and is unable to guess a reasonable threshold to separate inliers from outliers (as the one commonly used in RANSAC).
One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers
We propose the first general and practical framework to design certifiable algorithms for robust geometric perception in the presence of a large amount of outliers.
Monitoring and Diagnosability of Perception Systems
Towards this goal, we draw connections with the literature on self-diagnosability for multiprocessor systems, and generalize it to (i) account for modules with heterogeneous outputs, and (ii) add a temporal dimension to the problem, which is crucial to model realistic perception systems where modules interact over time.
Online Monitoring for Neural Network Based Monocular Pedestrian Pose Estimation
We investigate the problem of online output monitoring for neural networks that estimate 3D human shapes and poses from images.
3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans
Our second contribution is to provide the first fully automatic Spatial PerceptIon eNgine(SPIN) to build a DSG from visual-inertial data.
TEASER: Fast and Certifiable Point Cloud Registration
We propose the first fast and certifiable algorithm for the registration of two sets of 3D points in the presence of large amounts of outlier correspondences.
In Perfect Shape: Certifiably Optimal 3D Shape Reconstruction from 2D Landmarks
We study the problem of 3D shape reconstruction from 2D landmarks extracted in a single image.
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
We provide an open-source C++ library for real-time metric-semantic visual-inertial Simultaneous Localization And Mapping (SLAM).
DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams
This paper introduces DOOR-SLAM, a fully distributed SLAM system with an outlier rejection mechanism that can work with less conservative parameters.
Robotics
Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection
In this paper, we enable the simultaneous use of non-minimal solvers and robust estimation by providing a general-purpose approach for robust global estimation, which can be applied to any problem where a non-minimal solver is available for the outlier-free case.
A Quaternion-based Certifiably Optimal Solution to the Wahba Problem with Outliers
Our first contribution is to formulate the Wahba problem using a Truncated Least Squares (TLS) cost that is insensitive to a large fraction of spurious correspondences.
Outlier-Robust Spatial Perception: Hardness, General-Purpose Algorithms, and Guarantees
First, we show that even a simple linear instance of outlier rejection is inapproximable: in the worst-case one cannot design a quasi-polynomial time algorithm that computes an approximate solution efficiently.
A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates
We propose a robust approach for the registration of two sets of 3D points in the presence of a large amount of outliers.
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
We propose instead to tightly couple mesh regularization and state estimation by detecting and enforcing structural regularities in a novel factor-graph formulation.
Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models
Perceptual aliasing is one of the main causes of failure for Simultaneous Localization and Mapping (SLAM) systems operating in the wild.
Robotics 65K05, 62F10, 68T40, 68W40, 68W25, I.2.9; G.1.6
Accelerated Inference in Markov Random Fields via Smooth Riemannian Optimization
We show that this approach, named Fast Unconstrained SEmidefinite Solver (FUSES), can solve large problems in milliseconds.
Convex Relaxations for Pose Graph Optimization with Outliers
Pose Graph Optimization involves the estimation of a set of poses from pairwise measurements and provides a formalization for many problems arising in mobile robotics and geometric computer vision.
Sparse Depth Sensing for Resource-Constrained Robots
We address the following question: is it possible to reconstruct the geometry of an unknown environment using sparse and incomplete depth measurements?
Distributed Mapping with Privacy and Communication Constraints: Lightweight Algorithms and Object-based Models
Our field tests show that the combined use of our distributed algorithms and object-based models reduces the communication requirements by several orders of magnitude and enables distributed mapping with large teams of robots in real-world problems.
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it.
Robotics
On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
However, real-time optimization quickly becomes infeasible as the trajectory grows over time, this problem is further emphasized by the fact that inertial measurements come at high rate, hence leading to fast growth of the number of variables in the optimization.
Robotics
Pose Graph Optimization in the Complex Domain: Lagrangian Duality, Conditions For Zero Duality Gap, and Optimal Solutions
Our analysis shows that the duality gap is connected to the number of eigenvalues of the penalized pose graph matrix, which arises from the solution of the dual.
Robotics 68W01, 68W40, 68W25, 49K30 I.2.9; G.1.6
