论文标题
在大室内环境中评估RGB-D大满贯
Evaluation of RGB-D SLAM in Large Indoor Environments
论文作者
论文摘要
同时本地化和映射（SLAM）是控制系统的关键组件之一，旨在确保在未知环境中移动机器人的自主导航。在各种实际情况下，机器人可能需要长距离旅行才能完成任务。这需要大满贯方法的长期工作并构建大型地图。因此，计算负担（包括用于地图存储的高内存消耗）成为瓶颈。确实，最新的大满贯算法包括针对这一挑战的特定技术和优化，在长期情景中仍然需要适当评估。为此，我们对两种广泛的最先进的RGB-D SLAM方法进行了经验评估，适用于长期导航，即RTAB-MAP和VOXGRAPH。我们在大型模拟室内环境中评估它们，包括走廊和大厅，同时改变里程表噪声以进行更现实的设置。我们对两种方法都揭示了它们的优势和劣势提供定性和定量分析。我们发现，两种方法都构建了具有低探光噪声的高质量图，但使用高探光噪声会失败。与RTAB-MAP相比，VoxGraph具有较低的相对轨迹估计误差和内存消耗，而其绝对误差则更高。
Simultaneous localization and mapping (SLAM) is one of the key components of a control system that aims to ensure autonomous navigation of a mobile robot in unknown environments. In a variety of practical cases a robot might need to travel long distances in order to accomplish its mission. This requires long-term work of SLAM methods and building large maps. Consequently the computational burden (including high memory consumption for map storage) becomes a bottleneck. Indeed, state-of-the-art SLAM algorithms include specific techniques and optimizations to tackle this challenge, still their performance in long-term scenarios needs proper assessment. To this end, we perform an empirical evaluation of two widespread state-of-the-art RGB-D SLAM methods, suitable for long-term navigation, i.e. RTAB-Map and Voxgraph. We evaluate them in a large simulated indoor environment, consisting of corridors and halls, while varying the odometer noise for a more realistic setup. We provide both qualitative and quantitative analysis of both methods uncovering their strengths and weaknesses. We find that both methods build a high-quality map with low odometry noise but tend to fail with high odometry noise. Voxgraph has lower relative trajectory estimation error and memory consumption than RTAB-Map, while its absolute error is higher.
