Exosense: A Vision-Centric Scene Understanding System For Safe Exoskeleton Navigation

• URL: http://arxiv.org/abs/2403.14320v1
• Date: Thu, 21 Mar 2024 11:41:39 GMT
• Title: Exosense: A Vision-Centric Scene Understanding System For Safe Exoskeleton Navigation
• Authors: Jianeng Wang, Matias Mattamala, Christina Kassab, Lintong Zhang, Maurice Fallon,
• Abstract summary: Exosense is a vision-centric scene understanding system. It generates rich, globally-consistent elevation maps, incorporating both semantic and terrain traversability information. We demonstrate the system's robustness to the challenges of typical periodic walking gaits.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exoskeletons for daily use by those with mobility impairments are being developed. They will require accurate and robust scene understanding systems. Current research has used vision to identify immediate terrain and geometric obstacles, however these approaches are constrained to detections directly in front of the user and are limited to classifying a finite range of terrain types (e.g., stairs, ramps and level-ground). This paper presents Exosense, a vision-centric scene understanding system which is capable of generating rich, globally-consistent elevation maps, incorporating both semantic and terrain traversability information. It features an elastic Atlas mapping framework associated with a visual SLAM pose graph, embedded with open-vocabulary room labels from a Vision-Language Model (VLM). The device's design includes a wide field-of-view (FoV) fisheye multi-camera system to mitigate the challenges introduced by the exoskeleton walking pattern. We demonstrate the system's robustness to the challenges of typical periodic walking gaits, and its ability to construct accurate semantically-rich maps in indoor settings. Additionally, we showcase its potential for motion planning -- providing a step towards safe navigation for exoskeletons.

Related papers

• AdaGlimpse: Active Visual Exploration with Arbitrary Glimpse Position and Scale [2.462953128215088]
  Active Visual Exploration (AVE) is a task that involves dynamically selecting observations (glimpses) Existing mobile platforms equipped with optical zoom capabilities can capture glimpses of arbitrary positions and scales. AdaGlimpse uses Soft Actor-Critic, a reinforcement learning algorithm tailored for exploration tasks, to select glimpses of arbitrary position and scale.
  arXiv  Detail & Related papers  (2024-04-04T14:35:49Z)
• VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation [3.837186701755568]
  This paper explores the potential of Large Language Models in zero-shot anomaly detection for safe visual navigation. The proposed framework can identify anomalies within camera-captured frames that include any possible obstacles, then generate concise, audio-delivered descriptions emphasizing abnormalities.
  arXiv  Detail & Related papers  (2024-03-19T03:55:39Z)
• Floor extraction and door detection for visually impaired guidance [78.94595951597344]
  Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people.
  arXiv  Detail & Related papers  (2024-01-30T14:38:43Z)
• Autonomous Marker-less Rapid Aerial Grasping [5.892028494793913]
  We propose a vision-based system for autonomous rapid aerial grasping. We generate a dense point cloud of the detected objects and perform geometry-based grasp planning. We show the first use of geometry-based grasping techniques with a flying platform.
  arXiv  Detail & Related papers  (2022-11-23T16:25:49Z)
• Coupling Vision and Proprioception for Navigation of Legged Robots [65.59559699815512]
  We exploit the complementary strengths of vision and proprioception to achieve point goal navigation in a legged robot. We show superior performance compared to wheeled robot (LoCoBot) baselines. We also show the real-world deployment of our system on a quadruped robot with onboard sensors and compute.
  arXiv  Detail & Related papers  (2021-12-03T18:59:59Z)
• 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)
• Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations [75.60524561611008]
  This work aims to exploit the use of sparse visual observations to achieve perceptual locomotion over a range of commonly seen bumps, ramps, and stairs in human-centred environments. We first formulate the selection of minimal visual input that can represent the uneven surfaces of interest, and propose a learning framework that integrates such exteroceptive and proprioceptive data. We validate the learned policy in tasks that require omnidirectional walking over flat ground and forward locomotion over terrains with obstacles, showing a high success rate.
  arXiv  Detail & Related papers  (2021-09-28T20:25:10Z)
• 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)
• Structured Scene Memory for Vision-Language Navigation [155.63025602722712]
  We propose a crucial architecture for vision-language navigation (VLN) It is compartmentalized enough to accurately memorize the percepts during navigation. It also serves as a structured scene representation, which captures and disentangles visual and geometric cues in the environment.
  arXiv  Detail & Related papers  (2021-03-05T03:41:00Z)

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.