Unsupervised Fast Visual Localization and Mapping with Slow Features.

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.