# EM-Fusion: Dynamic Object-Level SLAM with Probabilistic Data Association

## TL;DR

This paper introduces EM-Fusion, a dynamic SLAM method that models moving objects with probabilistic data association, enabling robust and accurate dense object-level mapping in dynamic environments.

## Contribution

It presents a novel probabilistic approach for dynamic SLAM that represents objects with volumetric SDF maps and aligns RGB-D data directly, improving robustness and accuracy.

## Key findings

- Outperforms state-of-the-art methods in robustness
- Achieves higher accuracy in dynamic environments
- Effectively handles occlusions and data association

## Abstract

The majority of approaches for acquiring dense 3D environment maps with RGB-D cameras assumes static environments or rejects moving objects as outliers. The representation and tracking of moving objects, however, has significant potential for applications in robotics or augmented reality. In this paper, we propose a novel approach to dynamic SLAM with dense object-level representations. We represent rigid objects in local volumetric signed distance function (SDF) maps, and formulate multi-object tracking as direct alignment of RGB-D images with the SDF representations. Our main novelty is a probabilistic formulation which naturally leads to strategies for data association and occlusion handling. We analyze our approach in experiments and demonstrate that our approach compares favorably with the state-of-the-art methods in terms of robustness and accuracy.