Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach

• URL: http://arxiv.org/abs/2310.02650v2
• Date: Wed, 8 May 2024 20:48:52 GMT
• Title: Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach
• Authors: Matthew Hanlon, Boyang Sun, Marc Pollefeys, Hermann Blum,
• Abstract summary: This work investigates how active visual localization can be used to overcome challenges of viewpoint changes. Specifically, we focus on the problem of selecting the optimal viewpoint at a given location. The result demonstrates the superior performance of the data-driven approach when compared to existing methods.
• Score: 47.373245682678515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rather than having each newly deployed robot create its own map of its surroundings, the growing availability of SLAM-enabled devices provides the option of simply localizing in a map of another robot or device. In cases such as multi-robot or human-robot collaboration, localizing all agents in the same map is even necessary. However, localizing e.g. a ground robot in the map of a drone or head-mounted MR headset presents unique challenges due to viewpoint changes. This work investigates how active visual localization can be used to overcome such challenges of viewpoint changes. Specifically, we focus on the problem of selecting the optimal viewpoint at a given location. We compare existing approaches in the literature with additional proposed baselines and propose a novel data-driven approach. The result demonstrates the superior performance of the data-driven approach when compared to existing methods, both in controlled simulation experiments and real-world deployment.

Related papers

• Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information [68.10033984296247]
  This paper explores the domain of active localization, emphasizing the importance of viewpoint selection to enhance localization accuracy. Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications.
  arXiv  Detail & Related papers  (2024-07-22T12:32:09Z)
• PRISM-TopoMap: Online Topological Mapping with Place Recognition and Scan Matching [42.74395278382559]
  This paper introduces PRISM-TopoMap -- a topological mapping method that maintains a graph of locally aligned locations. The proposed method involves learnable multimodal place recognition paired with the scan matching pipeline for localization and loop closure. We conduct a broad experimental evaluation of the suggested approach in a range of photo-realistic environments and on a real robot.
  arXiv  Detail & Related papers  (2024-04-02T06:25:16Z)
• NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration [57.15811390835294]
  This paper describes how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration. We show that this unified policy results in better overall performance when navigating to visually indicated goals in novel environments. Our experiments, conducted on a real-world mobile robot platform, show effective navigation in unseen environments in comparison with five alternative methods.
  arXiv  Detail & Related papers  (2023-10-11T21:07:14Z)
• Reinforcement Learning with Frontier-Based Exploration via Autonomous Environment [0.0]
  This research combines an existing Visual-Graph SLAM known as ExploreORB with reinforcement learning. The proposed algorithm aims to improve the efficiency and accuracy of ExploreORB by optimizing the exploration process of frontiers to build a more accurate map.
  arXiv  Detail & Related papers  (2023-07-14T12:19:46Z)
• Challenges in Visual Anomaly Detection for Mobile Robots [65.53820325712455]
  We consider the task of detecting anomalies for autonomous mobile robots based on vision. We categorize relevant types of visual anomalies and discuss how they can be detected by unsupervised deep learning methods.
  arXiv  Detail & Related papers  (2022-09-22T13:26:46Z)
• Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning [60.599223456298915]
  We propose a novel way to integrate deep learning into exploration by leveraging 3D scene completion for informed, safe, and interpretable mapping and planning. We show that our method can speed up coverage of an environment by 73% compared to the baselines with only minimal reduction in map accuracy. Even if scene completions are not included in the final map, we show that they can be used to guide the robot to choose more informative paths, speeding up the measurement of the scene with the robot's sensors by 35%.
  arXiv  Detail & Related papers  (2022-08-17T14:19:33Z)
• 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)
• Learning to Shift Attention for Motion Generation [55.61994201686024]
  One challenge of motion generation using robot learning from demonstration techniques is that human demonstrations follow a distribution with multiple modes for one task query. Previous approaches fail to capture all modes or tend to average modes of the demonstrations and thus generate invalid trajectories. We propose a motion generation model with extrapolation ability to overcome this problem.
  arXiv  Detail & Related papers  (2021-02-24T09:07:52Z)
• A Collaborative Visual SLAM Framework for Service Robots [14.41737199910213]
  We present a collaborative visual simultaneous localization and mapping (SLAM) framework for service robots. Each robot can register to an existing map, update the map, or build new maps, all with a unified interface and low computation and memory cost. A landmark retrieval method is proposed to allow each robot to get nearby landmarks observed by others.
  arXiv  Detail & Related papers  (2021-02-05T15:19:07Z)

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.