A Deep Analysis of Visual SLAM Methods for Highly Automated and Autonomous Vehicles in Complex Urban Environment

In the context of automated driving, navigating through challenging urban environments with dynamic objects, large-scale scenes, and varying lighting/weather conditions, achieving accurate localization is paramount for highly-automated (HAVs) or autonomous vehicles (AVs). An imprecise localization can greatly impact subsequent decision-making to manage an HAV or AV's motion (planning and control tasks). In recent years, visual simultaneous localization and mapping (VSLAM) has shown substantial progress and equipping it can lead to handling non-standardized situations of real-world scenes and achieving higher localization and mapping accuracy. In this article, we present a comprehensive analysis of the current research status of VSLAM and its potential application to HAV or AV operating in complex urban environments. We first discuss the criteria to assess how well for the solutions that VSLAM methods offer to address the challenges, which include real-time performance, accuracy, robustness, and system operating cost. By employing these assessment criteria, we evaluate various VSLAM methods in four essential aspects including rejection and tracking of high dynamic objects, map construction in large-scale environments, loop detection and error correction, and sustainable operation and map updating. This evaluation provides valuable insights into the effectiveness of different VSLAM techniques. We then discuss potential research directions for leveraging VSLAM methods in achieving high-level automated driving in complex settings. We hope this article to serve as a timely update on recent progress and advances in VSLAM which are applicable to HAVs or AVs. To facilitate future research, we create a repository that includes links to relevant reviews and methodological papers for learning at https://github.com/bumblebee15138/VSLAM for HAVs and AVs.