Towards Open World NeRF-Based SLAM

Neural Radiance Fields (NeRFs) have taken the machine vision and robotics perception communities by storm and are starting to be applied in robotics applications. NeRFs offer versatility and robustness in map representations for Simultaneous Localization and Mapping. However, computational difficulties of multilayer perceptrons (MLP) have lead to reductions in robustness in the state-of-the-art of NeRF-based SLAM algorithms in order to meet real-time requirements. In this report, we seek to improve accuracy and robustness of NICE-SLAM, a recent NeRF-based SLAM algorithm, by accounting for depth measurement uncertainty and using IMU measurements. Additionally, extend this algorithm by providing a model that can represent backgrounds that are too distant to be modeled by NeRF.