A key feature in the context of simultaneous localization and mapping is loop-closure detection, a process determining whether the current robot’s environment perception coincides with previous observation. However, in long-term operations, both computational efficiency and memory requirements involved in an autonomous robot operation in uncontrolled environments, are of particular importance. The majority of approaches scale linearly with the environment’s size in terms of storage and query time. The article at hand presents an efficient appearance-based loop-closure detection pipeline, which encodes the traversed trajectory by a low amount of unique visual words generated on-line through feature tracking. The incrementally constructed visual vocabulary is referred to as the “Bag of Tracked Words.” A nearest-neighbor voting scheme is utilized to query the database and assign probabilistic scores to all visited locations. Exploiting the inherent temporal coherency in the loop-closure task, the produced scores are processed through a Bayesian filter to estimate the belief state about the robot’s location on the map. Also, a geometrical verification step ensures consistency between image matches. Management is also applied to the resulting vocabulary to reduce its growth rate and constraint the system’s computational complexity while improving its voting distinctiveness. The proposed approach’s performance is experimentally evaluated on several publicly available and challenging datasets, including hand-held, car-mounted, aerial, and ground trajectories. Results demonstrate the method’s adaptability, which retains high operational frequency in environments of up to 13 km and high recall rates for perfect precision, outperforming other state-of-the-art techniques. The system’s effectiveness is owed to the reduced vocabulary size, which is at least one order of magnitude smaller than other contemporary approaches. An open research-oriented source code has been made publicly available, which is dubbed as “BoTW-LCD.”

在同时定位和映射的情况下，关键功能是闭环检测，该过程确定当前机器人的环境感知是否与先前的观察相符。但是，在长期操作中，不受控制的环境中的自主机器人操作所涉及的计算效率和存储要求都特别重要。在存储和查询时间方面，大多数方法都根据环境的大小线性扩展。这篇文章提出了一种有效的基于外观的闭环检测管道，该管道通过特征跟踪在线生成的少量唯一视觉单词对遍历的轨迹进行编码。逐步构造的视觉词汇被称为“单词跟踪包”。最近邻居投票方案用于查询数据库，并为所有访问过的位置分配概率得分。利用闭环任务中固有的时间一致性，通过贝叶斯过滤器处理产生的分数，以估计关于机器人在地图上位置的置信状态。此外，几何验证步骤可确保图像匹配之间的一致性。管理还应用于最终的词汇表，以降低其增长率并限制系统的计算复杂性，同时提高其投票的独特性。在几种公开可用且具有挑战性的数据集（包括手持，车载，空中和地面轨迹）上，对所提出方法的性能进行了实验评估。结果证明了该方法的适应性，在长达13 km的环境中仍可保持较高的工作频率，并具有较高的召回率，以实现完美的精度，其性能优于其他最新技术。该系统的有效性归因于减少的词汇量，该词汇量比其他当代方法小至少一个数量级。面向研究的开放源代码已公开可用，被称为“ BoTW-LCD”。