SLAM for Indoor Parking: A Comprehensive Benchmark Dataset and a Tightly Coupled Semantic Framework

For the task of autonomous indoor parking, various Visual-Inertial Simultaneous Localization And Mapping (SLAM) systems are expected to achieve comparable results with the benefit of complementary effects of visual cameras and the Inertial Measurement Units. To compare these competing SLAM systems, it is necessary to have publicly available datasets, offering an objective way to demonstrate the pros/cons of each SLAM system. However, the availability of such high-quality datasets is surprisingly limited due to the profound challenge of the groundtruth trajectory acquisition in the Global Positioning Satellite denied indoor parking environments. In this article, we establish BeVIS, a large-scale Benchmark dataset with Visual (front-view), Inertial and Surround-view sensors for evaluating the performance of SLAM systems developed for autonomous indoor parking, which is the first of its kind where both the raw data and the groundtruth trajectories are available. In BeVIS, the groundtruth trajectories are obtained by tracking artificial landmarks scattered in the indoor parking environments, whose coordinates are recorded in a surveying manner with a high-precision Electronic Total Station. Moreover, the groundtruth trajectories are comprehensively evaluated in terms of two respects, the reprojection error and the pose volatility, respectively. Apart from BeVIS, we propose a novel tightly coupled semantic SLAM framework, namely VISSLAM-2, leveraging Visual (front-view), Inertial, and Surround-view sensor modalities, specially for the task of autonomous indoor parking. It is the first work attempting to provide a general form to model various semantic objects on the ground. Experiments on BeVIS demonstrate the effectiveness of the proposed VISSLAM-2. Our benchmark dataset BeVIS is publicly available at https://shaoxuan92.github.io/BeVIS.