Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking
This addresses the challenge of precise navigation for autonomous vehicles in complex indoor environments like parking lots, representing an incremental improvement in domain-specific localization.
The paper tackled the problem of real-time indoor localization for autonomous driving in parking lots by using high-level landmarks like parking slots and visual fiducial markers, achieving an average accuracy of 0.3m track tracing at 10kph.
In this paper, we proposed a novel and practical solution for the real-time indoor localization of autonomous driving in parking lots. High-level landmarks, the parking slots, are extracted and enriched with labels to avoid the aliasing of low-level visual features. We then proposed a robust method for detecting incorrect data associations between parking slots and further extended the optimization framework by dynamically eliminating suboptimal data associations. Visual fiducial markers are introduced to improve the overall precision. As a result, a semantic map of the parking lot can be established fully automatically and robustly. We experimented the performance of real-time localization based on the map using our autonomous driving platform TiEV, and the average accuracy of 0.3m track tracing can be achieved at a speed of 10kph.