uPLAM: Robust Panoptic Localization and Mapping Leveraging Perception Uncertainties

The availability of a reliable map and a robust localization system is critical for the operation of an autonomous vehicle. In a modern system, both mapping and localization solutions generally employ convolutional neural network (CNN) –based perception. Hence, any algorithm should consider potential errors in perception for safe and robust functioning. In this work, we present uncertainty-aware panoptic Localization and Mapping (uPLAM), which employs perception uncertainty as a bridge to fuse the perception information with classical localization and mapping approaches. We introduce an uncertainty-based map aggregation technique to create a long-term panoptic bird's eye view map and provide an associated mapping uncertainty. Our map consists of surface semantics and landmarks with unique IDs. Moreover, we present panoptic uncertainty-aware particle filter-based localization. To this end, we propose an uncertainty-based particle importance weight calculation for the adaptive incorporation of perception information into localization. We also present a new dataset for evaluating long-term panoptic mapping and map-based localization. Extensive evaluations showcase that our proposed uncertainty incorporation leads to better mapping with reliable uncertainty estimates and accurate localization. We make our dataset and code available at: