  Simultaneous localization and mapping (SLAM) with implicit neural
representations has received extensive attention due to the expressive
representation power and the innovative paradigm of continual learning.
However, deploying such a system within a dynamic environment has not been
well-studied. Such challenges are intractable even for conventional algorithms
since observations from different views with dynamic objects involved break the
geometric and photometric consistency, whereas the consistency lays the
foundation for joint optimizing the camera pose and the map parameters. In this
paper, we best exploit the characteristics of continual learning and propose a
novel SLAM framework for dynamic environments. While past efforts have been
made to avoid catastrophic forgetting by exploiting an experience replay
strategy, we view forgetting as a desirable characteristic. By adaptively
controlling the replayed buffer, the ambiguity caused by moving objects can be
easily alleviated through forgetting. We restrain the replay of the dynamic
objects by introducing a continually-learned classifier for dynamic object
identification. The iterative optimization of the neural map and the classifier
notably improves the robustness of the SLAM system under a dynamic environment.
Experiments on challenging datasets verify the effectiveness of the proposed
framework.
