# Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor

arXiv: https://arxiv.org/abs/2109.02786 · HTML: https://mlsanity.com/paper/2109.02786/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [A Theoretical Analysis of Deep Q-Learning](https://mlsanity.com/paper/1901.00137/) (2019)
- [Q-learning with Nearest Neighbors](https://mlsanity.com/paper/1802.03900/) (2018)
- [Supervised Learning of the Next-Best-View for 3D Object Reconstruction](https://mlsanity.com/paper/1905.05833/) (2019)
- [SPOT: A framework for selection of prototypes using optimal transport](https://mlsanity.com/paper/2103.10159/) (2021)
- [Deep Active Localization](https://mlsanity.com/paper/1903.01669/) (2019)
- [LiPo-LCD: Combining Lines and Points for Appearance-based Loop Closure Detection](https://mlsanity.com/paper/2009.09897/) (2020)
- [Detection-by-Localization: Maintenance-Free Change Object Detector](https://mlsanity.com/paper/1809.05267/) (2018)

## Built on by (most cited first, 1 of 1)

- [Compressive Self-localization Using Relative Attribute Embedding](https://mlsanity.com/paper/2208.08863/) (2022)

## Abstract (arXiv)

Landmark-based robot self-localization has recently garnered interest as a highly-compressive domain-invariant approach for performing visual place recognition (VPR) across domains (e.g., time of day, weather, and season). However, landmark-based self-localization can be an ill-posed problem for a passive observer (e.g., manual robot control), as many viewpoints may not provide an effective landmark view. In this study, we consider an active self-localization task by an active observer and present a novel reinforcement learning (RL)-based next-best-view (NBV) planner. Our contributions are as follows. (1) SIMBAD-based VPR: We formulate the problem of landmark-based compact scene description as SIMBAD (similarity-based pattern recognition) and further present its deep learning extension. (2) VPR-to-NBV knowledge transfer: We address the challenge of RL under uncertainty (i.e., active self-localization) by transferring the state recognition ability of VPR to the NBV. (3) NNQL-based NBV: We regard the available VPR as the experience database by adapting nearest-neighbor approximation of Q-learning (NNQL). The result shows an extremely compact data structure that compresses both the VPR and NBV into a single incremental inverted index. Experiments using the public NCLT dataset validated the effectiveness of the proposed approach.