MapCVV: On-Cloud Map Construction Using Crowdsourcing Visual Vectorized Elements Towards Autonomous Driving

Abstract

Maps are indispensable foundation for autonomous driving to provide prior knowledge of road structures. Constructing maps in a crowdsourcing manner has been a research hotspot in order to achieve lower map production cost and higher update frequency. Existing pipelines of crowdsourcing mapping construct scalar maps prior to on-cloud map vectorization. This leads to not only more demand of storage space and uploading bandwidth, but also the waste of computing resources because the on-cloud map vectorization is doing the similar work as the on-vehicle scene perception. To solve this issue, the letter proposes a novel, lightweight and efficient crowdsourcing mapping pipeline coined MapCVV. The pipeline directly consumes visual vectorized elements over the shelf provided by on-vehicle cognition and outputs vectorized map which can be used for localization, planning and navigation. Considering the lack of local rigidity in visual vectorized elements, traditional submap-level optimization will not be applicable. To cope with this challenge, we propose the novel element-level optimization that can achieve better global consistency among multiple runs when dealing with visual vectorized elements. Finally, we validate the practicability of the proposed MapCVV pipeline by conducting a 445km real-world experiment. The experiment shows that our method outperforms others with regard to global consistency and absolute accuracy. The uploading bandwidth for crowdsourcing data is only 2.6 MB/km. The average processing speed is notably 1.84 s/km.