Data Fusion for Sparse Semantic Localization Based on Object Detection

Abstract

Semantic information has started to be used in localization methods to introduce a non-geometric distinction in the environment. However, efficient ways to integrate this information remain a question. We propose an approach for fusing data from different object classes by analyzing the posterior for each object class to improve robustness and accuracy for self-localization. Our system uses the bearing angle to the objects’ center and objects’ class names as sensor model input to localize the user on a 2D annotated map consisting of objects’ class names and center coordinates. Sensor model input is obtained by an object detector on equirectangular images of a 360° field of view camera. As object detection performance varies based on location and object class, different object classes generate different likelihoods. We account for this by using appropriate weights generated by a Gaussian process model trained by using our posterior analysis. Our approach follows a systematic way to fuse data from different object classes and use them as a likelihood function of a Monte Carlo localization (MCL) algorithm.