Comparison — OpenVINS vs VINS-Mono vs OKVIS vs ORB-SLAM3

Table of contents

Quick Summary

A compact comparison of four widely used visual-inertial (VIO/VISLAM) systems.

• OpenVINS — A filter-based VIO framework using EKF and sliding-window estimation, focusing on lightweight, consistent estimation and extensibility.
• VINS-Mono — A tightly-coupled nonlinear optimization-based monocular VIO system with robust initialization, loop closure, and high accuracy.
• OKVIS — A keyframe-based nonlinear optimization VIO system emphasizing bundle adjustment and marginalization strategies.
• ORB-SLAM3 — A feature-based SLAM framework supporting visual, visual-inertial, and multi-map modes with mapping and loop closure.

Algorithmic Approach

OpenVINS

OpenVINS uses a filter-based estimation framework:

• Error-state Extended Kalman Filter (ES-EKF)
• Sliding window of camera states
• IMU preintegration
• Modular estimator architecture

Its main advantage is computational efficiency and predictable runtime, making it suitable for embedded and real-time applications.

VINS-Mono

VINS-Mono uses nonlinear optimization:

• Sliding-window optimization
• IMU preintegration
• Visual-inertial residual optimization
• Bundle adjustment style estimation

It provides:

• Robust initialization
• Failure recovery
• High estimation accuracy

It is widely used in aerial robotics and handheld devices.

OKVIS

OKVIS (Open Keyframe-based Visual-Inertial SLAM) adopts:

• Keyframe-based optimization
• Nonlinear bundle adjustment
• IMU preintegration
• Marginalization strategies

The keyframe mechanism maintains computational efficiency while preserving estimation accuracy.

ORB-SLAM3

ORB-SLAM3 is a feature-based SLAM framework using:

• Feature extraction and matching
• Bundle adjustment
• Visual-inertial estimation
• Loop closure
• Multi-map management

It supports:

• Monocular cameras
• Stereo cameras
• RGB-D cameras
• Visual-inertial sensors

Accuracy, Robustness, and Drift

Optimization-based methods generally achieve higher absolute accuracy than lightweight filtering approaches.

Optimization-based systems

Including:

• VINS-Mono
• OKVIS
• ORB-SLAM3

Advantages:

• Higher trajectory accuracy
• Better global consistency
• Improved drift correction

They benefit from:

• Sliding-window optimization
• Bundle adjustment
• Loop closure

Filter-based systems

OpenVINS provides:

• Lower computational cost
• Lower memory usage
• More predictable latency

The trade-off is slightly lower peak accuracy compared with optimization-based approaches.

Features and Practical Differences

Sensor Support

Mapping and Loop Closure

ORB-SLAM3

Provides full SLAM capabilities:

• Loop closure
• Map reuse
• Multi-map management
• Global optimization

Suitable for persistent mapping applications.

VINS-Mono

Provides:

• Loop detection
• Pose graph optimization
• Global trajectory correction

It is mainly designed as a VIO system with additional mapping capabilities.

OpenVINS and OKVIS

Primarily focus on odometry:

• OpenVINS emphasizes filter-based VIO research.
• OKVIS emphasizes accurate keyframe optimization.

Initialization and Failure Recovery

VINS-Mono

Strong points:

• Robust initialization
• Failure detection
• Recovery mechanisms

ORB-SLAM3

Uses:

• IMU initialization
• MAP estimation strategies
• Multi-mode tracking recovery

OpenVINS

Provides:

• EKF-based initialization
• Flexible estimator design

It is especially suitable for researchers developing new estimation methods.

Compute and Real-Time Constraints

Optimization-based approaches usually require:

• More CPU resources
• More memory
• Higher computational capability

Codebase and Community

OpenVINS

Advantages:

• Research-oriented architecture
• Modular implementation
• Calibration tools
• Easy algorithm modification

VINS-Mono

Advantages:

• Mature implementation
• Large research community
• Many robotics applications

OKVIS

Advantages:

• Classical academic implementation
• Good reference for keyframe VIO methods

ORB-SLAM3

Advantages:

• Large community
• Extensive downstream applications
• Strong benchmark performance

Typical Benchmark Behavior

Common evaluation datasets include:

• EuRoC MAV
• TUM-VI
• KITTI

General observations:

• ORB-SLAM3 and VINS-Mono achieve excellent results on many benchmarks.
• Optimization-based methods usually outperform filter-based approaches in absolute trajectory error.
• OpenVINS provides better computational efficiency.

Recommendations

Choose OpenVINS if you need:

• Lightweight VIO
• Embedded deployment
• Deterministic latency
• Research on state estimation algorithms

Choose VINS-Mono if you need:

• High-accuracy monocular VIO
• Robust initialization
• Aerial robotics applications

Choose OKVIS if you need:

• Classical keyframe-based VIO
• Accurate bundle adjustment
• Reference implementation for optimization methods

Choose ORB-SLAM3 if you need:

• Full SLAM system
• Mapping
• Loop closure
• Multi-sensor support
• Persistent environments

Decision Table