Fundamental Visual Navigation Algorithms: Indirect Sequential, Biased Diffusive, & Direct Pathing

• URL: http://arxiv.org/abs/2407.13535v1
• Date: Thu, 18 Jul 2024 14:07:44 GMT
• Title: Fundamental Visual Navigation Algorithms: Indirect Sequential, Biased Diffusive, & Direct Pathing
• Authors: Patrick Govoni, Pawel Romanczuk,
• Abstract summary: We study embodied neural networks to explore information processing algorithms an organism may use for visual spatial navigation. Surprisingly, three distinct classes of algorithms emerged, each with its own set of rules and tradeoffs, and each appear to be highly relevant to observable biological navigation behaviors.
• Score: 1.3812010983144802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effective foraging in a predictable local environment requires coordinating movement with observable spatial context - in a word, navigation. Distinct from search, navigating to specific areas known to be valuable entails its own particularities. How space is understood through vision and parsed for navigation is often examined experimentally, with limited ability to manipulate sensory inputs and probe into the algorithmic level of decision-making. As a generalizable, minimal alternative to empirical means, we evolve and study embodied neural networks to explore information processing algorithms an organism may use for visual spatial navigation. Surprisingly, three distinct classes of algorithms emerged, each with its own set of rules and tradeoffs, and each appear to be highly relevant to observable biological navigation behaviors.

Related papers

• MR.NAVI: Mixed-Reality Navigation Assistant for the Visually Impaired [42.45301319345154]
  We present MR. NAVI, a mixed reality system that enhances spatial awareness for visually impaired users.<n>Our system combines computer vision algorithms for object detection and depth estimation with natural language processing to provide contextual scene descriptions.
  arXiv  Detail & Related papers  (2025-05-28T14:02:56Z)
• NavigateDiff: Visual Predictors are Zero-Shot Navigation Assistants [24.689242976554482]
  Navigating unfamiliar environments presents significant challenges for household robots. Existing reinforcement learning methods cannot be directly transferred to new environments. We try to transfer the logical knowledge and the generalization ability of pre-trained foundation models to zero-shot navigation.
  arXiv  Detail & Related papers  (2025-02-19T17:27:47Z)
• Hierarchical end-to-end autonomous navigation through few-shot waypoint detection [0.0]
  Human navigation is facilitated through the association of actions with landmarks. Current autonomous navigation schemes rely on accurate positioning devices and algorithms as well as extensive streams of sensory data collected from the environment. We propose a hierarchical end-to-end meta-learning scheme that enables a mobile robot to navigate in a previously unknown environment.
  arXiv  Detail & Related papers  (2024-09-23T00:03:39Z)
• Learning Navigational Visual Representations with Semantic Map Supervision [85.91625020847358]
  We propose a navigational-specific visual representation learning method by contrasting the agent's egocentric views and semantic maps. Ego$2$-Map learning transfers the compact and rich information from a map, such as objects, structure and transition, to the agent's egocentric representations for navigation.
  arXiv  Detail & Related papers  (2023-07-23T14:01:05Z)
• Emergence of Maps in the Memories of Blind Navigation Agents [68.41901534985575]
  Animal navigation research posits that organisms build and maintain internal spatial representations, or maps, of their environment. We ask if machines -- specifically, artificial intelligence (AI) navigation agents -- also build implicit (or'mental') maps. Unlike animal navigation, we can judiciously design the agent's perceptual system and control the learning paradigm to nullify alternative navigation mechanisms.
  arXiv  Detail & Related papers  (2023-01-30T20:09:39Z)
• ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints [94.60414567852536]
  Long-range navigation requires both planning and reasoning about local traversability. We propose a learning-based approach that integrates learning and planning. ViKiNG can leverage its image-based learned controller and goal-directed to navigate to goals up to 3 kilometers away.
  arXiv  Detail & Related papers  (2022-02-23T02:14:23Z)
• Explore before Moving: A Feasible Path Estimation and Memory Recalling Framework for Embodied Navigation [117.26891277593205]
  We focus on the navigation and solve the problem of existing navigation algorithms lacking experience and common sense. Inspired by the human ability to think twice before moving and conceive several feasible paths to seek a goal in unfamiliar scenes, we present a route planning method named Path Estimation and Memory Recalling framework. We show strong experimental results of PEMR on the EmbodiedQA navigation task.
  arXiv  Detail & Related papers  (2021-10-16T13:30:55Z)
• Lifelong Topological Visual Navigation [16.41858724205884]
  We propose a learning-based visual navigation method with graph update strategies that improve lifelong navigation performance over time. We take inspiration from sampling-based planning algorithms to build image-based topological graphs, resulting in sparser graphs yet with higher navigation performance compared to baseline methods. Unlike controllers that learn from fixed training environments, we show that our model can be finetuned using a relatively small dataset from the real-world environment where the robot is deployed.
  arXiv  Detail & Related papers  (2021-10-16T06:16:14Z)
• Augmented reality navigation system for visual prosthesis [67.09251544230744]
  We propose an augmented reality navigation system for visual prosthesis that incorporates a software of reactive navigation and path planning. It consists on four steps: locating the subject on a map, planning the subject trajectory, showing it to the subject and re-planning without obstacles. Results show how our augmented navigation system help navigation performance by reducing the time and distance to reach the goals, even significantly reducing the number of obstacles collisions.
  arXiv  Detail & Related papers  (2021-09-30T09:41:40Z)
• Deep Learning for Embodied Vision Navigation: A Survey [108.13766213265069]
  "Embodied visual navigation" problem requires an agent to navigate in a 3D environment mainly rely on its first-person observation. This paper attempts to establish an outline of the current works in the field of embodied visual navigation by providing a comprehensive literature survey.
  arXiv  Detail & Related papers  (2021-07-07T12:09:04Z)
• Pushing it out of the Way: Interactive Visual Navigation [62.296686176988125]
  We study the problem of interactive navigation where agents learn to change the environment to navigate more efficiently to their goals. We introduce the Neural Interaction Engine (NIE) to explicitly predict the change in the environment caused by the agent's actions. By modeling the changes while planning, we find that agents exhibit significant improvements in their navigational capabilities.
  arXiv  Detail & Related papers  (2021-04-28T22:46:41Z)
• Occupancy Anticipation for Efficient Exploration and Navigation [97.17517060585875]
  We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions. By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment. Our approach is the winning entry in the 2020 Habitat PointNav Challenge.
  arXiv  Detail & Related papers  (2020-08-21T03:16:51Z)

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.