BioSLAM: A Bioinspired Lifelong Memory System for General Place Recognition
BioSLAM: A Bioinspired Lifelong Memory System for General Place Recognition
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DOI：
10.1109/tro.2023.3306615
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发表时间：
2022-08
影响因子：
7.8
通讯作者：
Peng Yin;Abulikemu Abuduweili;Shiqi Zhao;Changliu Liu;S. Scherer
中科院分区：
文献类型：
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作者：
Peng Yin;Abulikemu Abuduweili;Shiqi Zhao;Changliu Liu;S. Scherer
我们提出了BioSLAM，一个终身（终身同时定位和映射）SLAM框架，用于增量学习各种新的外观，并保持对以前访问过的区域的准确位置识别。与人类不同，人工神经网络遭受灾难性的遗忘，当用新到达的人训练时，可能会忘记以前访问过的区域。对于人类，研究人员发现大脑中存在一种记忆重放机制，以保持神经元对以前事件的活跃。受到这一发现的启发，BioSLAM设计了一个门控生成重放，以根据反馈奖励控制机器人的学习行为。具体来说，BioSLAM提供了一种新的双重记忆机制，用于维护：1）动态记忆，以有效地学习新的观察结果; 2）静态记忆，以平衡新旧知识。当代理在新的域下遇到不同的外观时，完整的处理管道可以帮助增量地更新位置识别能力，对长期位置识别的日益复杂性具有鲁棒性。我们在以下三个增量SLAM场景中演示了BioSLAM。1)一个120公里的城市规模的轨迹与激光雷达为基础的输入。2)一个多人访问的4.5公里校园规模的轨迹与激光雷达视觉输入。3)牛津官方数据集，在不同环境条件下具有10公里的视觉输入。我们表明，BioSLAM可以逐步更新代理的位置识别能力，并优于最先进的增量方法，生成重放，在位置识别准确性方面的24%。据我们所知，BioSLAM是第一个记忆增强的终身SLAM系统，有助于在长期导航任务中进行增量位置识别。
We present BioSLAM, a lifelong (lifelong simultaneous localization and mapping) SLAM framework for learning various new appearances incrementally and maintaining accurate place recognition for previously visited areas. Unlike humans, artificial neural networks suffer from catastrophic forgetting and may forget the previously visited areas when trained with new arrivals. For humans, researchers discover that there exists a memory replay mechanism in the brain to keep the neuron active for previous events. Inspired by this discovery, BioSLAM designs a gated generative replay to control the robot's learning behavior based on the feedback rewards. Specifically, BioSLAM provides a novel dual-memory mechanism for the maintenance of: 1) a dynamic memory to efficiently learn new observations; and 2) a static memory to balance new–old knowledge. When the agent is encountered with different appearances under new domains, the complete processing pipeline can help to incrementally update the place recognition ability, robust to the increasing complexity of long-term place recognition. We demonstrate BioSLAM in three incremental SLAM scenarios as follows. 1) A 120 km city-scale trajectories with LiDAR-based inputs. 2) A multivisited 4.5 km campus-scale trajectories with LiDAR-vision inputs. 3) An official Oxford dataset with 10 km visual inputs under different environmental conditions. We show that BioSLAM can incrementally update the agent's place recognition ability and outperform the state-of-the-art incremental approach, generative replay, by 24% in terms of place recognition accuracy. To the best of our knowledge, BioSLAM is the first memory-enhanced lifelong SLAM system to help incremental place recognition in long-term navigation tasks.