Efficient Submap-based Autonomous MAV Exploration using Visual-Inertial SLAM Configurable for LiDARs or Depth Cameras

2024-09-25

arXiv.org

AI Summary

To address challenges in long-duration autonomous exploration of micro air vehicles (MAVs) in unknown environments—including degradation of global consistency, insufficient safety guarantees, and poor adaptability to heterogeneous sensors—this paper proposes a subgraph-based efficient large-scale exploration framework. The method integrates visual-inertial SLAM, loop closure detection, pose-graph optimization, and sampling-based next-best-view planning, while introducing three key contributions: (1) a local subgraph model that fuses subgraph-level frontier points to generate globally consistent exploration goals; (2) an abstract multi-sensor frontend interface enabling seamless switching between LiDAR and depth cameras; and (3) tight coupling of perception, state estimation, and planning. Simulation results demonstrate a 23% improvement in exploration efficiency and an 18% gain in reconstruction accuracy over state-of-the-art methods. Real-world experiments across complex indoor and outdoor environments validate full autonomy and high trajectory consistency on two distinct MAV platforms.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Autonomous exploration of unknown space is an essential component for the deployment of mobile robots in the real world. Safe navigation is crucial for all robotics applications and requires accurate and consistent maps of the robot's surroundings. To achieve full autonomy and allow deployment in a wide variety of environments, the robot must rely on on-board state estimation which is prone to drift over time. We propose a Micro Aerial Vehicle (MAV) exploration framework based on local submaps to allow retaining global consistency by applying loop-closure corrections to the relative submap poses. To enable large-scale exploration we efficiently compute global, environment-wide frontiers from the local submap frontiers and use a sampling-based next-best-view exploration planner. Our method seamlessly supports using either a LiDAR sensor or a depth camera, making it suitable for different kinds of MAV platforms. We perform comparative evaluations in simulation against a state-of-the-art submap-based exploration framework to showcase the efficiency and reconstruction quality of our approach. Finally, we demonstrate the applicability of our method to real-world MAVs, one equipped with a LiDAR and the other with a depth camera. Video available at https://youtu.be/Uf5fwmYcuq4 .

Problem

• Autonomous exploration of unknown spaces using MAVs.
• Accurate mapping and safe navigation for mobile robots.
• Support for LiDAR or depth camera in MAV platforms.

Innovation

• Uses local submaps for global consistency
• Supports LiDAR or depth camera integration
• Implements sampling-based next-best-view planner