LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization (2310.07390v1)

Abstract: Precise and long-term stable localization is essential in parking lots for tasks like autonomous driving or autonomous valet parking, \textit{etc}. Existing methods rely on a fixed and memory-inefficient map, which lacks robust data association approaches. And it is not suitable for precise localization or long-term map maintenance. In this paper, we propose a novel mapping, localization, and map update system based on ground semantic features, utilizing low-cost cameras. We present a precise and lightweight parameterization method to establish improved data association and achieve accurate localization at centimeter-level. Furthermore, we propose a novel map update approach by implementing high-quality data association for parameterized semantic features, allowing continuous map update and refinement during re-localization, while maintaining centimeter-level accuracy. We validate the performance of the proposed method in real-world experiments and compare it against state-of-the-art algorithms. The proposed method achieves an average accuracy improvement of 5cm during the registration process. The generated maps consume only a compact size of 450 KB/km and remain adaptable to evolving environments through continuous update.

• The paper introduces an adaptive parameterization technique that robustly extracts ground semantic features for centimeter-level localization accuracy.
• It demonstrates efficient mapping with compact storage (450 KB/km) using a combination of four surround-view cameras, IMU, and probabilistic filtering for real-world validations.
• The system continuously updates maps during re-localization, providing a scalable solution for evolving parking lot environments and paving the way for broader autonomous driving applications.

Overview of LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots

The paper "LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization" presents a novel methodology for enhancing the self-localization capabilities of autonomous vehicles in parking lot scenarios. Unlike traditional mapping techniques that are often limited by static and inefficiently large maps, this work introduces a system to dynamically create, update, and maintain semantic maps using low-cost cameras.

Key Contributions

The paper makes several significant contributions to the field of autonomous vehicle localization:

1. Novel Parameterization Method: A major contribution is the development of an adaptive parameterization approach for ground semantic features. By focusing on ground-level semantic markers like arrows and parking lines, the method ensures more robust data association and efficient pose estimation, crucial for achieving centimeter-level localization accuracy.
2. Efficient and Lightweight Mapping: The authors introduce a mapping and localization pipeline that emphasizes efficiency and compactness. The resulting maps consume only 450 KB/km on average, rendering them easily maintainable and adaptable for long-term applications in evolving environments.
3. Dynamic Map Update Capability: The proposed system is equipped with a novel map update approach. It allows continuous refinement and augmentation of the map during re-localization events, maintaining or even enhancing the precision of localization in response to environmental changes.

Methodology and Evaluation

The proposed LESS-Map system is implemented using four surround-view cameras, an IMU, and wheel encoders to collect environmental data. The system's pipeline encompasses mapping, localization, and map updates. Key features of this pipeline include robust semantic segmentation, contour parameterization, and a two-step odometry and localization process grounded in a probabilistic filtering approach.

The efficacy of LESS-Map is validated through real-world experiments in outdoor and indoor parking scenarios. Comparison with existing state-of-the-art algorithms reveals a considerable improvement in localization accuracy and robustness. For instance, the system achieves an average registration accuracy enhancement of 5 cm over its predecessors. Importantly, the efficiency of LESS-Map is demonstrated by a significantly reduced map size, a critical factor for real-world deployment in intelligent vehicles.

Implications and Future Directions

The LESS-Map system has both practical and theoretical implications for the development of autonomous vehicle technologies. Practically, its lightweight design and adaptability offer a feasible solution for commercial adoption, enabling robust and reliable vehicle operation in complex environments such as parking lots. Theoretically, the proposed parameterization and map update strategies provide a robust framework for future research on scalable and maintainable localization systems.

Looking forward, this work sets a precedent for more generalized applications in varied autonomous driving settings beyond parking lots. Furthermore, extending this to multi-agent systems could enhance collaborative navigation strategies. The framework opens potential for integration with emerging technologies such as neural network-driven perception to further refine precision and adaptability.

Conclusion

This paper provides a significant advance in the domain of autonomous vehicle localization through the introduction of LESS-Map. By focusing on lightweight, efficient, and dynamically updateable maps using ground semantic features, it offers a compelling solution to challenges associated with static and voluminous mapping methods. The research presents a clear path towards future developments in adaptive and scalable mapping technologies for autonomous systems.