Multi-Session SLAM for Imaging Sonar Equipped Underwater Vehicles Using Semantic Scene Graphs

Abstract

Accurate, reliable state estimation is an essential component in underwater autonomy. In underwater environments, simultaneous localization and mapping (SLAM) is often employed to provide a robot with state estimates, given the lack of GPS. SLAM state estimates exhibit error growth over time, which can be mitigated by re-observing a previously visited location (place recognition). However, this is trajectory-dependent, prone to outlier measurements, and, if a robot is tasked with driving to a place recognition destination, it could cost critical resources such as fuel or battery life. This motivates the multi-session SLAM problem, where a robot finds place recognition between its own data and a prior SLAM mission, enhancing the quality and robustness of the current SLAM solution. We propose a semantic scene graph method that enables robust multi-session SLAM. We detect objects in the environment, build scene graphs, and propose methods for place recognition and for estimating rigid-body transforms between prior and current scene graphs. By using semantic scene graphs, we show robustness to outliers and degeneracy in underwater sonar data. Additionally, the proposed method is compact, allowing it to be stored with minimal memory cost and transmitted to robots with low latency. We validate our method across three real-world environments, performing ablation studies on the proposed system and benchmarking against the literature. The views expressed in this document are those of the author(s) and do not reflect the official policy or position of the U.S. Naval Academy, Department of the Navy, the Department of War, or the U.S. Government.