Current techniques in Visual Simultaneous Localization and Mapping (VSLAM) estimate camera displacement by comparing image features of consecutive scenes. These algorithms depend on scene continuity, hence requires frequent camera inputs. However, processing images frequently can lead to significant memory usage and computation overhead. In this study, we introduce SemanticSLAM, an end-to-end visual-inertial odometry system that utilizes semantic features extracted from an RGB-D sensor. This approach enables the creation of a semantic map of the environment and ensures reliable camera localization. SemanticSLAM is scene-agnostic, which means it doesn't require retraining for different environments. It operates effectively in indoor settings, even with infrequent camera input, without prior knowledge. The strength of SemanticSLAM lies in its ability to gradually refine the semantic map and improve pose estimation. This is achieved by a convolutional long-short-term-memory (ConvLSTM) network, trained to correct errors during map construction. Compared to existing VSLAM algorithms, SemanticSLAM improves pose estimation by 17%. The resulting semantic map provides interpretable information about the environment and can be easily applied to various downstream tasks, such as path planning, obstacle avoidance, and robot navigation. The code will be publicly available at https://github.com/Leomingyangli/SemanticSLAM

翻译：当前视觉同步定位与地图构建（VSLAM）技术通过比较连续场景的图像特征来估计相机位移。这些算法依赖于场景连续性，因此需要频繁的相机输入。然而，频繁处理图像会导致显著的内存占用和计算开销。本研究提出SemanticSLAM——一种利用RGB-D传感器提取语义特征的端到端视觉惯性里程计系统。该方法能够构建环境的语义地图并确保可靠的相机定位。SemanticSLAM具有场景无关性，即无需针对不同环境重新训练。它在室内环境中即使相机输入不频繁也能有效运行，且无需先验知识。SemanticSLAM的核心优势在于能够逐步优化语义地图并改进位姿估计，这是通过卷积长短期记忆（ConvLSTM）网络实现的，该网络在训练中专门纠正地图构建过程中的误差。与现有VSLAM算法相比，SemanticSLAM将位姿估计精度提升了17%。生成的语义地图提供关于环境的可解释信息，可便捷地应用于各类下游任务，如路径规划、避障和机器人导航。相关代码将开源在https://github.com/Leomingyangli/SemanticSLAM。