Distance Estimation to Support Assistive Drones for the Visually Impaired using Robust Calibration

• URL: http://arxiv.org/abs/2504.01988v1
• Date: Mon, 31 Mar 2025 08:56:17 GMT
• Title: Distance Estimation to Support Assistive Drones for the Visually Impaired using Robust Calibration
• Authors: Suman Raj, Bhavani A Madhabhavi, Madhav Kumar, Prabhav Gupta, Yogesh Simmhan,
• Abstract summary: We present NOVA, a robust calibration technique using depth maps to estimate absolute distances to obstacles in a campus environment.<n>We compare NOVA with SOTA depth map approaches, and with geometric and regression-based baseline models, for distance estimation to VIPs and other obstacles.<n>NOVA clearly out-performs SOTA depth map methods, by upto 5.3-14.6x.
• Score: 4.077787659104315
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Autonomous navigation by drones using onboard sensors, combined with deep learning and computer vision algorithms, is impacting a number of domains. We examine the use of drones to autonomously assist Visually Impaired People (VIPs) in navigating outdoor environments while avoiding obstacles. Here, we present NOVA, a robust calibration technique using depth maps to estimate absolute distances to obstacles in a campus environment. NOVA uses a dynamic-update method that can adapt to adversarial scenarios. We compare NOVA with SOTA depth map approaches, and with geometric and regression-based baseline models, for distance estimation to VIPs and other obstacles in diverse and dynamic conditions. We also provide exhaustive evaluations to validate the robustness and generalizability of our methods. NOVA predicts distances to VIP with an error <30cm and to different obstacles like cars and bicycles with a maximum of 60cm error, which are better than the baselines. NOVA also clearly out-performs SOTA depth map methods, by upto 5.3-14.6x.

Related papers

• NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
  We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
  arXiv  Detail & Related papers  (2025-06-23T14:28:30Z)
• Towards Perception-based Collision Avoidance for UAVs when Guiding the Visually Impaired [5.242869847419834]
  We present a perception-based path planning system for local planning around the neighborhood of the visually impaired.<n>We propose a multi DNN based framework for obstacle avoidance of the UAV as well as the VIP.<n>Our evaluations conducted on a drone human system in a university campus environment verifies the feasibility of our algorithms.
  arXiv  Detail & Related papers  (2025-06-17T09:08:30Z)
• Reinforcement Learning-Based Monocular Vision Approach for Autonomous UAV Landing [0.0]
  This paper introduces an innovative approach for the autonomous landing of Unmanned Aerial Vehicles (UAVs) using only a front-facing monocular camera.<n> Drawing on the inherent human estimating process, the proposed method reframes the landing task as an optimization problem.
  arXiv  Detail & Related papers  (2025-05-11T12:23:37Z)
• Long-Range Vision-Based UAV-assisted Localization for Unmanned Surface Vehicles [7.384309568198598]
  Global positioning system (GPS) has become an indispensable navigation method for field operations with unmanned surface vehicles (USVs) in marine environments. GPS may not always be available outdoors because it is vulnerable to natural interference and malicious jamming attacks. We present a novel method that utilizes an Unmanned Aerial Vehicle (UAV) to assist in localizing USVs in restricted marine environments.
  arXiv  Detail & Related papers  (2024-08-21T08:37:37Z)
• RoadBEV: Road Surface Reconstruction in Bird's Eye View [55.0558717607946]
  Road surface conditions, especially geometry profiles, enormously affect driving performance of autonomous vehicles. Vision-based online road reconstruction promisingly captures road information in advance. Bird's-Eye-View (BEV) perception provides immense potential to more reliable and accurate reconstruction. This paper uniformly proposes two simple yet effective models for road elevation reconstruction in BEV named RoadBEV-mono and RoadBEV-stereo.
  arXiv  Detail & Related papers  (2024-04-09T20:24:29Z)
• Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios [66.05091704671503]
  We present a novel angle navigation paradigm to deal with flight deviation in point-to-point navigation tasks. We also propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module.
  arXiv  Detail & Related papers  (2024-02-04T08:41:20Z)
• VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning [53.13393315664145]
  We present VAPOR, a novel method for autonomous legged robot navigation in unstructured, densely vegetated outdoor environments. Our method trains a novel RL policy using an actor-critic network and arbitrary data collected in real outdoor vegetation. We observe that VAPOR's actions improve success rates by up to 40%, decrease the average current consumption by up to 2.9%, and decrease the normalized trajectory length by up to 11.2%.
  arXiv  Detail & Related papers  (2023-09-14T16:21:27Z)
• CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning [1.0499611180329804]
  We propose a novel Deep Reinforcement Learning based algorithm, called CoverNav, for identifying covert and navigable trajectories in offroad terrains and jungle environments. CoverNav helps robot agents to learn the low elevation terrain using a reward function while penalizing it proportionately when it experiences high elevation. We evaluate CoverNav's effectiveness in achieving a maximum goal distance of 12 meters and its success rate in different elevation scenarios with and without cover objects.
  arXiv  Detail & Related papers  (2023-08-12T15:19:49Z)
• UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment [17.531224704021273]
  This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method. We propose a deep reinforcement learning (DRL)-based method combined with human-in-the-loop, which allows the UAV to avoid obstacles automatically during flying.
  arXiv  Detail & Related papers  (2023-04-07T01:44:05Z)
• AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning [63.628195002143734]
  We propose a novel approach for aerial video action recognition. Our method is designed for videos captured using UAVs and can run on edge or mobile devices. We present a learning-based approach that uses customized auto zoom to automatically identify the human target and scale it appropriately.
  arXiv  Detail & Related papers  (2023-03-02T21:24:19Z)
• TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos [57.92385818430939]
  Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. Existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge devices. We propose a simple yet effective framework, itTransVisDrone, that provides an end-to-end solution with higher computational efficiency.
  arXiv  Detail & Related papers  (2022-10-16T03:05:13Z)
• R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes [69.6715406227469]
  Self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches. We present R4Dyn, a novel set of techniques to use cost-efficient radar data on top of a self-supervised depth estimation framework.
  arXiv  Detail & Related papers  (2021-08-10T17:57:03Z)
• Robust Autonomous Landing of UAV in Non-Cooperative Environments based on Dynamic Time Camera-LiDAR Fusion [11.407952542799526]
  We construct a UAV system equipped with low-cost LiDAR and binocular cameras to realize autonomous landing in non-cooperative environments. Taking advantage of the non-repetitive scanning and high FOV coverage characteristics of LiDAR, we come up with a dynamic time depth completion algorithm. Based on the depth map, the high-level terrain information such as slope, roughness, and the size of the safe area are derived.
  arXiv  Detail & Related papers  (2020-11-27T14:47:02Z)
• Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach [36.587096293618366]
  An emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings. This paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets.
  arXiv  Detail & Related papers  (2020-01-13T23:23:09Z)

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.