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Coarse-to-Fine Contrast Maximization for Energy-Efficient Motion Estimation in Edge-Deployed Event-Based SLAM.

Kyeongpil Min1, Jongin Choi1, Woojoo Lee1

  • 1Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea.

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|February 27, 2026
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Summary
This summary is machine-generated.

We developed Coarse-to-Fine Contrast Maximization (CCMAX) for efficient event-based motion estimation. CCMAX significantly reduces computation and energy use in Simultaneous Localization and Mapping (SLAM) systems, achieving comparable accuracy to traditional methods.

Keywords:
FPGA prototypingcoarse-to-fine optimizationcontrast maximizationedge computingevent-based vision sensorlow-power designmotion estimationvisual simultaneous localization and mapping (visual SLAM)

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Event-based vision sensors offer high temporal resolution and low power, ideal for edge robotics and SLAM.
  • Contrast Maximization (CMAX) is a direct geometric method for ego-motion estimation using event data.
  • Conventional CMAX is computationally intensive due to processing full event sets and high-resolution images repeatedly.

Purpose of the Study:

  • To develop a computationally efficient variant of CMAX for event-based motion estimation.
  • To reduce the computational and energy costs of CMAX without sacrificing accuracy.
  • To enable real-time SLAM on resource-constrained edge platforms.

Main Methods:

  • Proposed Coarse-to-Fine Contrast Maximization (CCMAX), a computation-aware CMAX approach.
  • Implemented progressive IWE resolution increase and coarse-grid event subsampling in early stages.
  • Retained full-resolution refinement in the final optimization stage.

Main Results:

  • CCMAX achieved accuracy comparable to full-resolution CMAX on standard benchmarks.
  • Reduced floating-point operations (FLOPs) by up to 42%.
  • Demonstrated up to 87% lower energy consumption on a RISC-V edge SoC.

Conclusions:

  • CCMAX offers a significant reduction in computational cost and energy consumption for event-based motion estimation.
  • The proposed method is suitable for real-time edge SLAM on power- and resource-constrained platforms.
  • CCMAX presents an energy-efficient front-end for advanced robotics applications.