Semantic Localization Considering Uncertainty of Object Recognition

Semantics can be leveraged in ego-vehicle localization to improve robustness and accuracy because objects with the same labels can be correctly matched with each other. Object recognition has significantly improved owing to advances in machine learning algorithms. However, perfect object recognition is still challenging in real environments. Hence, the uncertainty of object recognition must be considered in localization. This letter proposes a novel localization method that integrates a supervised object recognition method, which predicts probabilistic distributions over object classes for individual sensor measurements, into the Bayesian network for localization. The proposed method uses the estimated probabilities and Dirichlet distribution to calculate the likelihood for estimating an ego-vehicle pose. Consequently, the uncertainty can be handled in localization. We present an implementation example of the proposed method using a particle filter and deep-neural-network-based point cloud semantic segmentation and evaluate it by simulation and the SemanticKITTI dataset. Experimental results show that the proposed method can accurately generate likelihood distribution even when object recognition accuracy is degraded, and its estimation accuracy is the highest compared to that of two conventional methods.