SMF-VO: Direct Ego-Motion Estimation via Sparse Motion Fields

• URL: http://arxiv.org/abs/2511.09072v1
• Date: Thu, 13 Nov 2025 01:30:23 GMT
• Title: SMF-VO: Direct Ego-Motion Estimation via Sparse Motion Fields
• Authors: Sangheon Yang, Yeongin Yoon, Hong Mo Jung, Jongwoo Lim,
• Abstract summary: We introduce Sparse Motion Field Visual Odometry (SMF-VO), a lightweight,'motion-centric' framework.<n>Our approach directly estimates instantaneous linear and angular velocity from sparse optical flow, bypassing the need for explicit pose estimation or expensive landmark tracking.<n>Our work establishes a scalable and efficient alternative to conventional methods, making it highly suitable for mobile robotics and wearable devices.
• Score: 4.710825549574638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional Visual Odometry (VO) and Visual Inertial Odometry (VIO) methods rely on a 'pose-centric' paradigm, which computes absolute camera poses from the local map thus requires large-scale landmark maintenance and continuous map optimization. This approach is computationally expensive, limiting their real-time performance on resource-constrained devices. To overcome these limitations, we introduce Sparse Motion Field Visual Odometry (SMF-VO), a lightweight, 'motion-centric' framework. Our approach directly estimates instantaneous linear and angular velocity from sparse optical flow, bypassing the need for explicit pose estimation or expensive landmark tracking. We also employed a generalized 3D ray-based motion field formulation that works accurately with various camera models, including wide-field-of-view lenses. SMF-VO demonstrates superior efficiency and competitive accuracy on benchmark datasets, achieving over 100 FPS on a Raspberry Pi 5 using only a CPU. Our work establishes a scalable and efficient alternative to conventional methods, making it highly suitable for mobile robotics and wearable devices.

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