SDF-guided Keyframe Selection: Novel Boost for NeRF SLAM Loop Closure

Abstract

In the domain of Simultaneous Localization and Mapping (SLAM), loop closure is a linchpin for achieving accurate and consistent 3D environment mapping. However, the process is fraught with abrupt light changes and motion blur. These elements introduce uncertainties and inaccuracies in the data captured by sensors, severely undermining the system’s robustness. To address this critical challenge, we present a novel SDF-guided keyframe selection algorithm tailored for loop closure. Our approach capitalizes on the geometric insights provided by the Signed Distance Function (SDF) to meticulously choose keyframes, effectively mitigating the impact of noisy data. By doing so, we enhance the reliability of loop closure, refine the accuracy of 3D map reconstructions, and fortify the overall stability of the system. Our algorithm’s efficacy is substantiated through comprehensive experiments on datasets like Replica, ScanNet, and Tum-RGBD. Notably, it can be easily integrated as a plug-and-play module into diverse existing methods, enhancing their performance across different scenarios. Real-world trials using a hand-held LeTMC-520 camera for indoor scene reconstruction further validate its practicality and effectiveness.