multi-view SLAM visualization

understanding optimization through multiple perspectives

Understanding SLAM requires seeing it from multiple angles - both literally and figuratively. This project showcases how different viewpoints reveal different aspects of the optimization process, helping us debug algorithms and communicate complex concepts.

The MIT outdoor dataset

This challenging multi-robot dataset captures the complexity of real-world SLAM:

Raw odometry from multiple robots - notice the drift accumulating over time. This is the input our SLAM algorithms must correct.

Multiple camera perspectives

Viewing the optimization from different angles helps us understand the 3D structure of the solution:

Three camera angles showing the same optimization process - each perspective reveals different geometric relationships in the problem.

Comparing optimization approaches

Side-by-side comparison of our CORA algorithm versus traditional methods:

Left: CORA converging to the global optimum. Right: GTSAM's local optimization. Both reach similar final solutions, but CORA provides guarantees.

Urban environments

SLAM in structured urban environments presents unique challenges with buildings, symmetry, and perceptual aliasing:

Urban plaza scenarios with multiple loop closures - watching the algorithm recognize when it has returned to a previously visited location.

Complex geometries

Left: Multi-level environment requiring 3D reasoning. Right: Large-scale outdoor navigation from the GOATS benchmark.

Why visualization matters

These animations are more than just pretty pictures - they’re essential tools for algorithm development (spotting bugs and unexpected behaviors), performance analysis (understanding where and why algorithms struggle), communication (explaining complex optimization concepts to diverse audiences), and education (teaching students about SLAM and robotics).

By seeing optimization unfold in real-time, we gain intuition that guides the development of better algorithms.

These visualizations use data from public benchmarks and our own experiments to demonstrate SLAM algorithm performance across diverse scenarios.