# Sensors, SLAM and Long-term Autonomy: A Review

arXiv: https://arxiv.org/abs/1807.01605 · HTML: https://mlsanity.com/paper/1807.01605/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey](https://mlsanity.com/paper/2402.13255/) (2024)
- [Visual SLAM: What are the Current Trends and What to Expect?](https://mlsanity.com/paper/2210.10491/) (2022)
- [Predictive Temporal Attention on Event-based Video Stream for Energy-efficient Situation Awareness](https://mlsanity.com/paper/2402.08936/) (2024)

## Abstract (arXiv)

Simultaneous Localization and Mapping, commonly known as SLAM, has been an active research area in the field of Robotics over the past three decades. For solving the SLAM problem, every robot is equipped with either a single sensor or a combination of similar/different sensors. This paper attempts to review, discuss, evaluate and compare these sensors. Keeping an eye on future, this paper also assesses the characteristics of these sensors against factors critical to the long-term autonomy challenge.