A Robust, Task-Agnostic and Fully-Scalable Voxel Mapping System for Large Scale Environments

TL;DR

This paper introduces a scalable, task-agnostic voxel mapping system that efficiently builds high-resolution maps for large environments, supporting multiple autonomous navigation tasks and multi-agent cooperation with minimal bandwidth.

Contribution

It presents a novel hash table-based voxel mapping system that is adaptable, scalable, and capable of multi-task and multi-agent applications with efficient map sharing.

Findings

Built a high-resolution, wide-coverage map in real-time

Achieved over 95% bandwidth reduction in map sharing

Demonstrated effectiveness in various navigation tasks

Abstract

Perception still remains a challenging problem for autonomous navigation in unknown environment, especially for aerial vehicles. Most mapping algorithms for autonomous navigation are specifically designed for their very intended task, which hinders extended usage or cooperative task. In this paper, we propose a voxel mapping system that can build an adaptable map for multiple tasks. The system employs hash table-based map structure and manages each voxel with spatial and temporal priorities without explicit map boundary. We also introduce an efficient map-sharing feature with minimal bandwidth to enable multi-agent applications. We tested the system in real world and simulation environment by applying it for various tasks including local mapping, global mapping, cooperative multi-agent navigation, and high-speed navigation. Our system proved its capability to build customizable map with high resolution, wide coverage, and real-time performance regardless of sensor and environment. The system can build a full-resolution map using the map-sharing feature, with over 95 % of bandwidth reduction from raw sensor data.