SemanticSLAM: 学習ベースのセマンティックマップ構築とロバストなカメラ自己位置推定
SemanticSLAM: Learning based Semantic Map Construction and Robust Camera Localization
RGB-Dセンサから抽出したセマンティック特徴を用い、ConvLSTMでマップ構築誤差を補正しながら環境のセマンティックマップ生成とカメラ位置推定を同時に行う視覚慣性オドメトリシステムを提案。
著者: Mingyang Li, Yue Ma, Qinru Qiu
分類: cs.RO, cs.CV
原文アブストラクト
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
