Data-Association-Free Object SLAM Framework Proposed

A generalized SLAM framework that does not rely on data associations simultaneously estimates robot poses, landmark positions, landmark semantics, and data associations using odometry along with positional and semantic measurements. This approach utilizes deep learning to obtain semantic details such as class labels and feature vectors. Furthermore, the accuracy is enhanced through a semi-incremental estimation method.