2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVSMR-3.7
Paper Title UNSUPERVISED FAST VISUAL LOCALIZATION AND MAPPING WITH SLOW FEATURES
Authors Muhammad Haris, Frankfurt University of Applied Sciences, Germany; Mathias Franzius, Honda Research Institute Europe GmbH, Germany; Ute Bauer-Wersing, Frankfurt University of Applied Sciences, Germany
SessionMLR-APPL-IVSMR-3: Machine learning for image and video sensing, modeling and representation 3
LocationArea D
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video sensing, modeling, and representation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Visual localization is the task of accurately estimating the camera's position in a known environment. State-of-the-art methods use the 3D structure of a scene for precise visual localization. However, 3D scene reconstruction is resource-intensive in terms of hardware requirements and computation time, making it infeasible to run on low-cost embedded hardware. Unsupervised spatial representation learning with Slow Feature Analysis (SFA) enables computationally inexpensive localization and mapping. This paper analyzes SFA-based and the well-known structure-based localization, i.e., Active Search, in two distinct settings: short-term temporal and extreme spatial generalization. We present the experimental results from an outdoor environment and compare both methods w.r.t localization accuracy and computation time. Results show that the SFA-based approach is 886x faster in mapping time and 34x faster in localization than Active Search while achieving comparable localization accuracy in our test scenario.