LT-Mem: Volatility-Aware Spatio-Temporal Memory for Lifelong Scene Understanding
Long-term robot operation in evolving environments requires object-level understanding that persists across repeated revisits. Existing systems either overwrite history to maintain an up-to-date map or store semantic snapshots without consistent cross-session object identity, resulting in temporal amnesia: the systematic loss of object history that prevents answering queries such as "Where has the green chair been across all sessions?" We propose LT-Mem, a volatility-aware memory evolution framework that unifies spatially aligned instance-level 3D perception with volatility-conditioned temporal reasoning. First, a multi-session SLAM backbone provides spatially aligned per-object observati...
| Authors | Yumin Lee, Hyoseok Ju, Giseop Kim |
|---|---|
| Primary category | cs.RO |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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