Distributed Visual-Inertial Cooperative Localization

• URL: http://arxiv.org/abs/2103.12770v1
• Date: Tue, 23 Mar 2021 18:12:07 GMT
• Title: Distributed Visual-Inertial Cooperative Localization
• Authors: Pengxiang Zhu, Patrick Geneva, Wei Ren, and Guoquan Huang
• Abstract summary: We present a consistent and distributed state estimator for multi-robot cooperative localization (CL) In particular, we leverage covariance intersection (CI) to allow each robot to only track its own state and autocovariance. The proposed distributed CL estimator is validated against its naive non-realtime centralized counterpart extensively in both simulations and real-world experiments.
• Score: 32.49338004445954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we present a consistent and distributed state estimator for multi-robot cooperative localization (CL) which efficiently fuses environmental features and loop-closure constraints across time and robots. In particular, we leverage covariance intersection (CI) to allow each robot to only track its own state and autocovariance and compensate for the unknown correlations between robots. Two novel different methods for utilizing common environmental temporal SLAM features are introduced and evaluated in terms of accuracy and efficiency. Moreover, we adapt CI to enable drift-free estimation through the use of loop-closure measurement constraints to other robots' historical poses without a significant increase in computational cost. The proposed distributed CL estimator is validated against its naive non-realtime centralized counterpart extensively in both simulations and real-world experiments.

Related papers

• Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
  We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data. Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
  arXiv  Detail & Related papers  (2024-04-24T09:04:36Z)
• Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization [1.0485739694839669]
  We focus on cooperative localization and observability analysis of relative pose estimation. For ROS/Gazebo simulations, we explore four sensing and communication structures. In hardware experiments, two Turtlebot3 equipped with UWB modules are used for real-world inter-robot relative pose estimation.
  arXiv  Detail & Related papers  (2024-01-27T06:09:56Z)
• Off Environment Evaluation Using Convex Risk Minimization [0.0]
  We propose a convex risk minimization algorithm to estimate the model mismatch between the simulator and the target domain. We show that this estimator can be used along with the simulator to evaluate performance of an RL agents in the target domain.
  arXiv  Detail & Related papers  (2021-12-21T21:31:54Z)
• Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation [87.85646257351212]
  We study the problem of multi-robot mapless navigation in the popular Training and Decentralized Execution (CTDE) paradigm. This problem is challenging when each robot considers its path without explicitly sharing observations with other robots. We propose a novel architecture for CTDE that uses a centralized state-value network to compute a joint state-value.
  arXiv  Detail & Related papers  (2021-12-16T16:47:00Z)
• Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation [22.63837164001751]
  Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations. To enable collaborative localization, four key challenges must be addressed, including modeling complex relationships between observed objects. We introduce a novel approach that integrates uncertainty-aware graph learning model and state estimation for a team of robots to collaboratively localize objects.
  arXiv  Detail & Related papers  (2021-11-05T22:48:13Z)
• An automatic differentiation system for the age of differential privacy [65.35244647521989]
  Tritium is an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML) We introduce Tritium, an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML)
  arXiv  Detail & Related papers  (2021-09-22T08:07:42Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Scalable Multi-Robot System for Non-myopic Spatial Sampling [9.37678298330157]
  This paper presents a scalable distributed multi-robot planning algorithm for non-uniform sampling of spatial fields. We analyze the effect of communication between multiple robots, acting independently, on the overall sampling performance of the team.
  arXiv  Detail & Related papers  (2021-05-20T20:30:10Z)
• Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment [60.31418655784291]
  We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
  arXiv  Detail & Related papers  (2021-03-06T09:10:03Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)
• Disentangled Sequence Clustering for Human Intention Inference [40.46123013107865]
  Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE) Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE)
  arXiv  Detail & Related papers  (2021-01-23T13:39:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.