# Efficient and robust Sensor Placement in Complex Environments

arXiv: https://arxiv.org/abs/2309.08545 · HTML: https://mlsanity.com/paper/2309.08545/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://mlsanity.com/paper/1611.09326/) (2016)
- [FaSTrack: a Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking](https://mlsanity.com/paper/2102.07039/) (2021)
- [Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation](https://mlsanity.com/paper/2012.03023/) (2020)
- [Depth by Poking: Learning to Estimate Depth from Self-Supervised Grasping](https://mlsanity.com/paper/2006.08903/) (2020)
- [Optimal Sensor Positioning (OSP); A Probability Perspective Study](https://mlsanity.com/paper/1604.05391/) (2016)
- [Greedy Algorithms for Sparse Sensor Placement via Deep Learning](https://mlsanity.com/paper/1809.06025/) (2018)

## Abstract (arXiv)

We address the problem of efficient and unobstructed surveillance or communication in complex environments. On one hand, one wishes to use a minimal number of sensors to cover the environment. On the other hand, it is often important to consider solutions that are robust against sensor failure or adversarial attacks. This paper addresses these challenges of designing minimal sensor sets that achieve multi-coverage constraints -- every point in the environment is covered by a prescribed number of sensors. We propose a greedy algorithm to achieve the objective. Further, we explore deep learning techniques to accelerate the evaluation of the objective function formulated in the greedy algorithm. The training of the neural network reveals that the geometric properties of the data significantly impact the network's performance, particularly at the end stage. By taking into account these properties, we discuss the differences in using greedy and ε-greedy algorithms to generate data and their impact on the robustness of the network.