Kai Liu has recently had his paper, “Visual Place Recognition via Robust L2-Norm Distance Based Holism and Landmark Integration,” accepted into the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019).

This work proposes a novel method to learn a location representation that can integrate the semantic landmarks of a place with its holistic representation. To promote the robustness of our new model against the drastic appearance variations due to long-term visual changes, we formulate our objective to use non-squared L2-norm distances, which leads to a difficult optimization problem that minimizes the ratio of the L2,1-norms of matrices. To solve our objective, we derive a new efficient iterative algorithm, whose convergence is rigorously guaranteed by theory. We evaluate the proposed method using two large-scale benchmark data sets, the CMU-VL and Nord-land data sets. Experimental results have validated the effectiveness of our new method in long-term visual place recognition applications.

This work will be presented this Jan. in Hawaii, US. AAAI is the top conference in Artificial Intelligence.