Data Association and Localization of Classified Objects in Visual SLAM

Abstract

Maps generated by many visual Simultaneous Localization and Mapping algorithms consist of geometric primitives such as points, lines or planes. These maps offer a topographic representation of the environment, but they contain no semantic information about the environments. Object classifiers leveraging advances in machine learning are highly accurate and reliable, capable of detecting and classifying thousands of objects. Classifiers can be incorporated into a SLAM pipeline to add semantic information to a scene. Frequently, this semantic information is conducted for each frame of the image, but semantic labeling is not persistent over time. In this work, we present a nonparametric statistical approach to perform matching/association of objects detected over consecutive image frames. These associated classified objects are then localized in the accrued map using an unsupervised clustering method. We test our approach on multiple data sets, and it shows strong performance in terms of objects correctly associated from frame to frame. We also have tested our algorithm on three data sets in our lab environment using tag markers to demonstrate the accuracy of classified object localization process.