Simultaneous localisation and mapping (SLAM) play a vital role in autonomous robotics. Robotic platforms are often resource-constrained, and this limitation motivates resource-efficient SLAM implementations. While sparse visual SLAM algorithms offer good accuracy for modest hardware requirements, even these more scalable sparse approaches face limitations when applied to large-scale and long-term scenarios. A contributing factor is that the point clouds resulting from SLAM are inefficient to use and contain significant redundancy. This paper proposes the use of subset selection algorithms to reduce the map produced by sparse visual SLAM algorithms. Information-theoretic techniques have been applied to simpler related problems before, but they do not scale if applied to the full visual SLAM problem. This paper proposes a number of novel information\hyp{}theoretic utility functions for map point selection and optimises these functions using greedy algorithms. The reduced maps are evaluated using practical data alongside an existing visual SLAM implementation (ORB-SLAM 2). Approximate selection techniques proposed in this paper achieve trajectory accuracy comparable to an offline baseline while being suitable for online use. These techniques enable the practical reduction of maps for visual SLAM with competitive trajectory accuracy. Results also demonstrate that SLAM front-end performance can significantly impact the performance of map point selection. This shows the importance of testing map point selection with a front-end implementation. To exploit this, this paper proposes an approach that includes a model of the front-end in the utility function when additional information is available. This approach outperforms alternatives on applicable datasets and highlights future research directions.

翻译：同步定位与地图构建（SLAM）在自主机器人领域发挥着至关重要的作用。机器人平台通常面临资源受限的挑战，这一限制推动了资源高效型SLAM实现的研究。尽管稀疏视觉SLAM算法在适度的硬件要求下能提供良好的精度，但即使这些更具可扩展性的稀疏方法在应用于大规模和长期场景时仍面临局限性。其成因之一是SLAM生成的点云使用效率低下且存在显著冗余。本文提出采用子集选择算法来缩减稀疏视觉SLAM算法生成的地图。信息论技术此前已被应用于更简单的相关问题，但若直接应用于完整的视觉SLAM问题则不具备可扩展性。本文提出了若干新颖的基于信息论的地图点选择效用函数，并采用贪心算法优化这些函数。通过结合现有视觉SLAM实现（ORB-SLAM 2）的实际数据对缩减后的地图进行评估。本文提出的近似选择技术在保持与离线基准相当轨迹精度的同时，适用于在线应用场景。这些技术使得视觉SLAM地图的实际缩减成为可能，且能保持具有竞争力的轨迹精度。实验结果还表明，SLAM前端性能会显著影响地图点选择的效果，这说明在测试地图点选择算法时必须结合前端实现。为利用这一发现，本文提出一种在可获取额外信息时将前端模型纳入效用函数的方法。该方法在适用数据集上的表现优于其他方案，并指明了未来研究方向。