A Two-step Nonlinear Factor Sparsification for Scalable Long-term SLAM Backend
Binqian Jiang,Shaojie Shen,Binqian Jiang,Shaojie Shen
This paper proposes a new nonlinear factor sparsification paradigm for general feature-based long-term SLAM backend. Given a pose sparsification policy, we aim to scale the SLAM problem with space explored instead of time in a principled way so that the number of time-indexed poses can be limited. At the same time, their influence and the long-lived landmarks are appropriately maintained. To do th...
