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RGB-D SLAM: A Review of Methods and Performance Trade-Offs for Different Requirements.

Yixin Yan¹, Jinling Li¹, Qiuyang Liu¹

¹The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.

Sensors (Basel, Switzerland)

|June 12, 2026

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This study introduces Red, Green, Blue plus Depth (RGB-D) Simultaneous Localization and Mapping (SLAM) systems. It details methods, discusses performance trade-offs, and guides future RGB-D SLAM development.

Area of Science:

Robotics and Computer Vision

Background:

RGB-D cameras are crucial onboard sensors for Simultaneous Localization and Mapping (SLAM).
Simultaneous Localization and Mapping using RGB-D data is a significant research area due to its comprehensive environmental sensing capabilities.

Purpose of the Study:

To provide a thorough overview of the RGB-D SLAM framework.
To analyze and discuss the performance trade-offs and metric conflicts in current RGB-D SLAM systems.

Main Methods:

Comprehensive review of RGB-D SLAM components and representative approaches.
Analysis of diverse performance metrics and their interdependencies.
Examination of system requirements and their impact on method selection.

Main Results:

Identification of challenges in achieving perfect performance in RGB-D SLAM.

Keywords:

RGB-D SLAM deep learning multi-sensor fusion performance trade-off

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Detailed examination of conflicts among various performance metrics.

Discussion of practical considerations for selecting RGB-D SLAM methods.

Conclusions:

RGB-D SLAM systems offer a practical reference for diverse applications.
Understanding performance trade-offs is essential for effective RGB-D SLAM implementation.
This work provides a structured foundation for future advancements in RGB-D SLAM technology.