Feasibility of Video-based Sub-meter Localization on Resource-constrained Platforms
While the satellite-based Global Positioning System (GPS) is adequate for some outdoor applications, many other applications are held back by its multi-meter positioning errors and poor indoor coverage. In this paper, we study the feasibility of real-time video-based localization on resource-constrained platforms. Before commencing a localization task, a video-based localization system downloads an offline model of a restricted target environment, such as a set of city streets, or an indoor shopping mall. The system is then able to localize the user within the model, using only video as input. To enable such a system to run on resource-constrained embedded systems or smartphones, we (a) propose techniques for efficiently building a 3D model of a surveyed path, through frame selection and efficient feature matching, (b) substantially reduce model size by multiple compression techniques, without sacrificing localization accuracy, (c) propose efficient and concurrent techniques for feature extraction and matching to enable online localization, (d) propose a method with interleaved feature matching and optical flow based tracking to reduce the feature extraction and matching time in online localization. Based on an extensive set of both indoor and outdoor videos, manually annotated with location ground truth, we demonstrate that sub-meter accuracy, at real-time rates, is achievable on smart-phone type platforms, despite challenging video conditions.
Code (0)
등록된 구현이 없습니다.
Tasks
Optical Flow EstimationSimilar Papers 제목 키워드 기반
RDTF: Resource-efficient Dual-mask Training Framework for Multi-frame Animated Sticker Generation
Recently, great progress has been made in video generation technology, attracting the widespread attention of scholars. To apply this technology to downstream applications under resource-constrained conditions, researche…
Video GenerationBenchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models
In temporal action localization, given an input video, the goal is to predict which actions it contains, where they begin, and where they end. Training and testing current state-of-the-art deep learning models requires a…
Action LocalizationBenchmarkingComputational EfficiencyTemporal Action LocalizationSeeing More with Less: Video Capsule Endoscopy with Multi-Task Learning
Video capsule endoscopy has become increasingly important for investigating the small intestine within the gastrointestinal tract. However, a persistent challenge remains the short battery lifetime of such compact sensor…
Multi-Task LearningAnomaly DetectionStructured Graph Network for Constrained Robot Crowd Navigation with Low Fidelity Simulation
We investigate the feasibility of deploying reinforcement learning (RL) policies for constrained crowd navigation using a low-fidelity simulator. We introduce a representation of the dynamic environment, separating human…
Reinforcement Learning (RL)PhysV2A: Reachability-Gated and Semantic-Mask-Constrained Feasibility Completion for Video-to-Robot Manipulation
Video-based manipulation provides object-centric motion priors from human demonstrations, generated videos, or RGB-D observations, but such priors are typically embodiment-agnostic and cannot be directly executed by a sp…
Robot Manipulation