Collaborative Exploration of VSLAM Based Autonomous Vehicles and Drones

Abstract

Collaborative exploration by unmanned vehicles and drones relies on precise positioning and mapping, as well as efficient path planning. However, inconsistencies in spatial references among cross-platform sensors and the lack of environmental description due to sparse features lead to relative pose estimation drift during collaboration, making it difficult to support high-precision navigation for autonomous vehicles. To address the aforementioned issues, this paper proposes an integrated air-ground collaborative exploration method that integrates collaborative localization and mapping technology with an improved JPS path planning algorithm. At the perception level, we improved the ORB-SLAM2 algorithm by introducing a 3D Gaussian field as the underlying map representation and combined it with the G-ICP algorithm to solve the problem of cross-view point cloud registration, thus constructing a dense environment map with both global consistency and detailed texture. This high-precision global prior information effectively compensates for the blind spots in the local perception of autonomous vehicles, providing reliable prior environmental information for path planning. Based on this, to address the problems of traditional planning algorithms being prone to local optima and poor trajectory smoothness during the exploration process, we have integrated an improved JPS algorithm with multi-dimensional constraints: by introducing dynamic weight factors to balance heuristic estimation and actual cost, and by using direction factors and line-of-sight detection mechanisms to optimize the search strategy. This method significantly improves the smoothness of the trajectory's compliance with the kinematic constraints of the autonomous vehicle while ensuring global optimality. Finally, complex scenario tests were conducted using a real heterogeneous robot collaborative platform. The results show that the success rate of collaborative exploration between the autonomous vehicle and the drone in complex environments reached 87.5%, validating the robustness and superiority of the proposed method in environmental map construction and path planning during collaborative exploration between drones and autonomous vehicles.