Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation

Abstract

Scene understanding is a pivotal task for autonomous vehicles to safely navigate in the environment. Recent advances in deep learning enable accurate semantic reconstruction of the surroundings from LiDAR data. However, these models encounter a large domain gap while deploying them on vehicles equipped with different LiDAR setups which drastically decreases their performance. Fine-tuning the model for every new setup is infeasible due to the expensive and cumbersome process of recording and manually labeling new data. Unsupervised Domain Adaptation (UDA) techniques are thus essential to fill this domain gap and retain the performance of models on new sensor setups without the need for additional data labeling. In this paper, we propose AdaptLPS, a novel UDA approach for LiDAR panoptic segmentation that leverages task-specific knowledge and accounts for variation in the number of scan lines, mounting position, intensity distribution, and environmental conditions. We tackle the UDA task by employing two complementary domain adaptation strategies, data-based and model-based. While databased adaptations reduce the domain gap by processing the raw LiDAR scans to resemble the scans in the target domain, modelbased techniques guide the network in extracting features that are representative for both domains. Extensive evaluations on three pairs of real-world autonomous driving datasets demonstrate that AdaptLPS outperforms existing UDA approaches by up to 6.41 pp in terms of the PQ score.

Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping through Continual Learning

“…As illustrated in Fig. 1, the setting addressed in domain adaptation only considers unidirectional knowledge transfer from a single known to a single unknown environment [13] and thus does not represent the open world, where the number of new environments that a robot can encounter is infinite and previously known environments can be revisited.…”