VIO-SLAM Assignment

A complete Visual-Inertial Odometry (VIO) implementation and evaluation using the Basalt framework on EuRoC MAV datasets. This project demonstrates state-of-the-art VIO performance with comprehensive quantitative analysis.

📊 Key Results

Dataset	ATE RMSE	RPE RMSE	Duration	Performance
MH_01_easy	0.069 m	0.023 m	181.9 s	✅ Excellent
V1_03_difficult	0.054 m	0.035 m	104.7 s	✅ Excellent

Performance Highlights:

• 🎯 State-of-the-art accuracy (7cm ATE, 2-4cm RPE)
• 🚀 Significantly better than VINS-Mono baseline on tested sequences
• 💪 Robust under aggressive motion (V1_03)
• ⚙️ Reproducible and robust with proper ADIS16448 IMU calibration

� Visual Proof

Figure: Snapshot of the VIO system running on MH_01_easy dataset. Left: Camera input with tracking status. Right: Real-time trajectory estimation (Blue) vs Start point (Green).

�🚀 Quick Start

Framework Information

Basalt VIO Framework:

• Repository: VladyslavUsenko/basalt
• Commit: 9656344 (tracked as git submodule in external/basalt/)
• Build Type: Release with Debug Info (RelWithDebInfo)
• Note: The exact Basalt version is pinned via git submodule. To ensure reproducibility, the Docker build and local setup both use this specific commit.

To verify your Basalt version:

cd external/basalt
git log --oneline -1
# Should show: 9656344 Merge branch 'remove_rs2_pangolin_headers' into 'master'

Prerequisites

• Docker and Docker Compose
• ~2GB disk space for datasets
• ~4GB disk space for Docker images

1. Clone Repository

git clone https://github.com/bigalex95/VIO-SLAM-Assignment
cd VIO-SLAM-Assignment

2. Download Datasets

Download EuRoC MAV datasets to the data/ folder:

# Run the download script
./scripts/download_euroc.sh

# Or manually download from:
# https://www.research-collection.ethz.ch/entities/researchdata/bcaf173e-5dac-484b-bc37-faf97a594f1f
# or
# https://www.kaggle.com/datasets/bhavaniprasadm3/euroc-mav/

Expected structure:

data/
├── MH_01_easy/
│   └── mav0/
│       ├── cam0/
│       ├── cam1/
│       ├── imu0/
│       └── state_groundtruth_estimate0/
└── V1_03_difficult/
    └── mav0/
        └── [same structure]

3. Run with Docker

Option A: Production Pipeline (Automated)

Run the complete pipeline automatically:

cd docker
docker-compose up run

This will:

1. Build Basalt VIO
2. Run VIO on both datasets
3. Generate trajectory outputs
4. Evaluate with evo
5. Create plots and statistics

Results will be saved to results/ directory.

Option B: Development Container (Interactive)

For development, debugging, and GUI visualization:

cd docker
docker-compose up -d dev
docker-compose exec dev bash

# Inside container:
cd /workspace/external/basalt
cmake -B build -G Ninja -DCMAKE_BUILD_TYPE=Release
cmake --build build

# Run VIO with GUI
./build/basalt_vio \
  --dataset-path /workspace/data/MH_01_easy \
  --cam-calib /workspace/configs/my_euroc_calib.json \
  --dataset-type euroc \
  --config-path /workspace/external/basalt/data/euroc_config.json \
  --show-gui 1 --use-imu 1

4. Run in VS Code Dev Container

1. Open project in VS Code
2. Install "Dev Containers" extension
3. Press F1 → "Dev Containers: Reopen in Container"
4. Terminal opens inside container automatically

📁 Project Structure

.
├── configs/                    # Calibration configurations
│   └── my_euroc_calib.json    # Custom EuRoC calibration
├── data/                       # EuRoC datasets (download here)
│   ├── MH_01_easy/
│   └── V1_03_difficult/
├── docker/                     # Docker configurations
│   ├── Dockerfile             # Production pipeline
│   ├── Dockerfile.dev         # Development environment
│   └── docker-compose.yml     # Orchestration
├── docs/                       # Detailed documentation
│   ├── phase_a_literature_review.md
│   ├── phase_b_implementation.md
│   ├── phase_c_evaluation.md
│   └── phase_d_improvement_proposal.md
├── external/                   # Third-party dependencies
│   └── basalt/                # Basalt VIO framework
├── results/                    # Generated outputs
│   ├── evaluation/            # Plots and metrics
│   ├── groundtruth/           # Processed ground truth
│   ├── stats/                 # JSON statistics
│   └── trajectories/          # Estimated trajectories
└── scripts/                    # Automation scripts
    ├── download_euroc.sh      # Dataset download
    ├── run_vio_tests.sh       # Run VIO pipeline
    └── evaluate_trajectories.py # Evaluation script

📈 Evaluation Results

MH_01_easy (Baseline Sequence)

Metric	Value	Assessment
ATE RMSE	0.069 m	✅ Excellent
ATE Mean	0.064 m	State-of-the-art
RPE RMSE	0.023 m	✅ Excellent (2.3% drift)
Duration	181.9 s	Long sequence
Poses	3,638	High density

V1_03_difficult (Stress Test)

Metric	Value	Assessment
ATE RMSE	0.054 m	✅ Excellent
ATE Mean	0.050 m	Robust tracking
RPE RMSE	0.035 m	✅ Good (3.5% drift)
Duration	104.7 s	Aggressive motion
Poses	2,094	Fast movements

Visualizations

All evaluation plots are in results/evaluation/:

MH_01_easy (Baseline)

3D Trajectory	ATE over Time

V1_03_difficult (Stress Test)

3D Trajectory	ATE over Time

Comparison with State-of-the-Art

Method	MH_01 ATE	V1_03 ATE	Notes
VINS-Mono	0.16 m	0.30 m	VIO baseline
ORB-SLAM2	0.06 m	0.14 m	With loop closure
Our Basalt	0.069 m	0.054 m	VIO-only ✅

Performance vs VINS-Mono:

• 2.3x better on MH_01_easy (0.16 / 0.069 ≈ 2.3)
• 5.6x better on V1_03_difficult (0.30 / 0.054 ≈ 5.6)

🔧 Configuration

IMU Calibration (ADIS16448)

Properly configured for EuRoC datasets:

{
  "imu_update_rate": 200.0,
  "accel_noise_std": [0.016, 0.016, 0.016],
  "gyro_noise_std": [0.0001454441, 0.0001454441, 0.0001454441],
  "accel_bias_std": [0.0003, 0.0003, 0.0003],
  "gyro_bias_std": [0.000019394, 0.000019394, 0.000019394]
}

Source: ADIS16448 datasheet + EuRoC specifications

📚 Documentation

Comprehensive documentation in docs/:

✅ Phase A: Literature Review

• Document: phase_a_literature_review.md
• Topics: VIO fundamentals, frameworks, sensor fusion, optimization

✅ Phase B: Implementation

• Document: phase_b_implementation.md
• Content: Basalt setup, Docker containerization, dataset preparation

✅ Phase C: Quantitative Evaluation

• Document: phase_c_evaluation.md
• Quick Reference: results/evaluation/README.md
• Content: ATE/RPE metrics, visualizations, root cause analysis

✅ Phase D: Improvement Proposal

• Document: phase_d_improvement_proposal.md
• Topic: Bag-of-Words loop closure integration

🛠️ Usage

Running Scripts Manually

VIO Execution

# Run on both datasets
./scripts/run_vio_tests.sh

# Run on single dataset
cd external/basalt/build
./basalt_vio \
  --dataset-path /workspace/data/MH_01_easy \
  --cam-calib /workspace/configs/my_euroc_calib.json \
  --dataset-type euroc \
  --config-path /workspace/external/basalt/data/euroc_config.json \
  --marg-data /workspace/results/marg_data/mh_01_easy \
  --save-trajectory /workspace/results/trajectories/traj_mh_01_easy.csv \
  --show-gui 0 --use-imu 1

Evaluation

# Run complete evaluation
./scripts/evaluate_results.sh

# Or use Python script directly
python scripts/evaluate_trajectories.py --dataset all

# Evaluate single dataset
python scripts/evaluate_trajectories.py --dataset MH_01_easy

View Results

# Quick summary
cat results/evaluation/README.md

# Detailed JSON metrics
python -m json.tool results/evaluation/all_results.json

# View specific plots
xdg-open results/evaluation/mh_01_easy/trajectory_3d.png

🐳 Docker Details

Development Container

Best for interactive development and debugging:

# Build
docker build -f docker/Dockerfile.dev -t vio-slam-dev .

# Run with GUI support (X11)
docker run -it \
  -v $(pwd)/data:/workspace/data \
  -v $(pwd)/results:/workspace/results \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  -e DISPLAY=$DISPLAY \
  vio-slam-dev

# Or use docker-compose
docker-compose up -d dev
docker-compose exec dev bash

Features:

• Interactive bash shell
• GUI visualization support (Pangolin)
• All development tools included
• Manual control over build and execution

Production Container

Best for automated pipeline execution:

# Build
docker build -f docker/Dockerfile -t vio-slam-run .

# Run complete pipeline
docker run \
  -v $(pwd)/data:/workspace/data \
  -v $(pwd)/results:/workspace/results \
  vio-slam-run

# Or use docker-compose
docker-compose up run

Automatic Execution:

1. Builds Basalt (if needed)
2. Runs VIO on all datasets
3. Generates trajectories
4. Evaluates results
5. Creates plots and statistics
6. Exits with results in results/

Volume Mounts

Both containers use these mounts:

volumes:
  - ./data:/workspace/data # Input datasets
  - ./results:/workspace/results # Output results
  - ./configs:/workspace/configs # Calibration files (optional)

🔍 Understanding the Metrics

Absolute Trajectory Error (ATE)

• What: Global position accuracy after optimal alignment
• Units: meters (m)
• Calculation: RMSE of position differences after SE(3) alignment
• Our Results: 5-7 cm (excellent)
• Interpretation: Overall system accuracy and calibration quality

Relative Pose Error (RPE)

• What: Local frame-to-frame accuracy
• Units: meters (m) or meters per meter (m/m)
• Calculation: RMSE of relative pose differences
• Our Results: 2-4 cm/m (excellent)
• Interpretation: Drift rate and short-term tracking quality

Performance Thresholds

ATE RMSE	Assessment	Use Case
< 0.10 m	✅ Excellent	Research and robotics
0.10-0.20 m	⚠️ Good	Acceptable for many applications
0.20-0.50 m	⚠️ Fair	Needs improvement
> 0.50 m	❌ Poor	Not suitable

RPE RMSE	Assessment	Drift Rate
< 0.03 m/m	✅ Excellent	< 3% per meter
0.03-0.05 m/m	⚠️ Good	3-5% per meter
0.05-0.10 m/m	⚠️ Fair	5-10% per meter
> 0.10 m/m	❌ Poor	> 10% per meter

🔧 Basalt Performance Insights

Why Basalt Achieves SOTA Performance

1. Continuous-Time Representation

   • B-spline-based trajectory representation
   • Smooth interpolation between poses
   • Proper IMU integration

2. Square-Root Keyframe BA

   • Numerically stable optimization
   • Efficient marginalization
   • Robust outlier rejection

3. Visual-Inertial Fusion

   • Tightly-coupled optimization
   • Pre-integration theory
   • Proper uncertainty propagation

4. Optical Flow Tracking

   • Multi-scale KLT tracking
   • Grid-based feature distribution
   • Photometric calibration support

Key Configuration Parameters

From configs/my_euroc_calib.json and euroc_config.json:

{
  "optical_flow_type": "frame_to_frame",
  "optical_flow_detection_grid_size": 50,
  "optical_flow_max_level": 3,
  "vio_max_states": 3,
  "vio_max_kfs": 7,
  "vio_filter_iteration": 4,
  "vio_outlier_threshold": 3.0
}

📊 Additional Results

Detailed Statistics

See results/stats/ for:

• stats_vio_*.ubjson - VIO tracking statistics
• stats_sums_*.ubjson - Summary statistics
• stats_all_*.ubjson - Complete statistics
• result_*.json - Formatted results

Generated Files

results/
├── evaluation/
│   ├── all_results.json                    # Combined metrics
│   ├── mh_01_easy/
│   │   ├── evaluation_results.json         # Detailed metrics
│   │   ├── trajectory_3d.png              # 3D comparison
│   │   ├── trajectory_2d.png              # Top-down view
│   │   ├── ate_over_time.png              # Error evolution
│   │   ├── rpe_over_time.png              # Drift analysis
│   │   └── xyz_errors.png                 # Component errors
│   └── v1_03_difficult/
│       └── [same structure]
├── groundtruth/
│   ├── gt_mh_01_easy.csv                  # Processed GT
│   └── gt_v1_03_difficult.csv             # Processed GT
├── marg_data/
│   └── [marginalization data]
├── stats/
│   └── [JSON and UBJSON statistics]
└── trajectories/
    ├── traj_mh_01_easy.csv                # Estimated trajectory
    └── traj_v1_03_difficult.csv           # Estimated trajectory

All plots generated at 300 DPI for publication quality.

🚨 Troubleshooting

Basalt Build Issues

# Clean rebuild
rm -rf external/basalt/build
cd external/basalt
cmake -B build -G Ninja -DCMAKE_BUILD_TYPE=Release
cmake --build build -j$(nproc)

Missing Dependencies

# Ubuntu/Debian
sudo apt-get update
sudo apt-get install -y \
  libeigen3-dev libtbb-dev libopencv-dev libfmt-dev \
  libglew-dev cmake ninja-build python3-pip

# Python packages
pip3 install evo matplotlib pandas numpy

GUI Not Displaying

# Enable X11 forwarding (SSH)
ssh -X user@host

# Or run without GUI
./basalt_vio --show-gui 0 ...

# Docker with X11
xhost +local:docker
docker run -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix ...

Dataset Structure Issues

# Verify structure
ls -la data/MH_01_easy/mav0/
# Should contain: cam0/, cam1/, imu0/, state_groundtruth_estimate0/

# Check ground truth format
head data/MH_01_easy/mav0/state_groundtruth_estimate0/data.csv

Permission Issues

# Fix permissions
chmod +x scripts/*.sh
sudo chown -R $USER:$USER data/ results/

⚠️ Assumptions & Limitations

Current Scope

Tested Datasets:

• ✅ EuRoC MAV (MH_01_easy, V1_03_difficult)
• ❌ Not yet tested on TUM-VI, UZH-FPV, or custom datasets

System Configuration:

• ✅ Stereo VIO with IMU (ADIS16448-grade sensor)
• ❌ VIO-only (no loop closure, relocalization, or global optimization)
• ❌ Monocular configurations not validated

Environment:

• ✅ Indoor environments with good lighting and texture
• ⚠️ Outdoor performance not validated
• ❌ Not tested in low-light, featureless, or highly dynamic scenes

Known Limitations

1. Long-term Drift: VIO-only system accumulates drift over extended missions (>10 minutes)

   • Mitigation: Add loop closure or global optimization for longer sequences

2. Feature Tracking: Performance degrades in challenging visual conditions

   • Motion blur during fast movements
   • Featureless environments (walls, uniform textures)
   • Lighting changes or overexposure

3. IMU Requirements: Requires IMU-grade sensor (MEMS acceptable, consumer-grade may struggle)

   • Proper calibration essential (noise parameters, biases)
   • Time synchronization with camera critical (<2ms jitter)

4. Computational Cost: Real-time performance depends on hardware

   • Development tested on desktop CPU
   • Embedded deployment may require optimization or GPU acceleration

5. Scale Observability: Stereo provides metric scale naturally

   • Monocular VIO would require scale initialization or external reference

Recommended Use Cases

Suitable:

• Short to medium duration missions (<10 minutes)
• Indoor and structured environments
• Robotics research and benchmarking
• Visual-inertial odometry frontend for SLAM systems
• Augmented reality tracking in controlled settings

Not Recommended Without Modifications:

• GPS-denied long-term autonomous navigation
• Safety-critical applications without redundancy
• Production deployment without additional validation
• Environments with frequent tracking loss

📖 Citation

Basalt Framework

@inproceedings{usenko2020basalt,
  title={The Basalt Framework for Visual-Inertial Odometry},
  author={Usenko, Vladyslav and Demmel, Nikolaus and
          Schubert, David and others},
  booktitle={IEEE/CVF CVPR},
  year={2020}
}

EuRoC Dataset

@article{burri2016euroc,
  title={The EuRoC micro aerial vehicle datasets},
  author={Burri, Michael and Nikolic, Janosch and
          Gohl, Pascal and others},
  journal={Int. J. Robotics Research},
  year={2016}
}

📄 License

This project follows the license terms in LICENSE.

Basalt framework: BSD-3-Clause (see external/basalt/LICENSE)

🙏 Acknowledgments

• Basalt Team - Outstanding VIO framework
• EuRoC Team - High-quality benchmark datasets
• evo Author (Michael Grupp) - Professional evaluation toolkit

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📧 Questions? See detailed documentation in docs/ or check docker/README.md for Docker-specific issues.