# Learning Long-Range Perception Using Self-Supervision from Short-Range Sensors and Odometry

arXiv: https://arxiv.org/abs/1809.07207 · HTML: https://mlsanity.com/paper/1809.07207/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [Learning Visual Locomotion with Cross-Modal Supervision](https://mlsanity.com/paper/2211.03785/) (2022)
- [Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain](https://mlsanity.com/paper/2208.01329/) (2022)
- [Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective](https://mlsanity.com/paper/2407.04717/) (2024)
- [Learning a Model for Inferring a Spatial Road Lane Network Graph using Self-Supervision](https://mlsanity.com/paper/2107.01784/) (2021)
- [Sixth-Sense: Self-Supervised Learning of Spatial Awareness of Humans from a Planar Lidar](https://mlsanity.com/paper/2502.21029/) (2025)

## Abstract (arXiv)

We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor (such as a camera); we assume that the former is directly related to some piece of information to be perceived (such as the presence of an obstacle in a given position), whereas the latter is information-rich but hard to interpret directly. We instantiate and implement the approach on a small mobile robot to detect obstacles at various distances using the video stream of the robot's forward-pointing camera, by training a convolutional neural network on automatically-acquired datasets. We quantitatively evaluate the quality of the predictions on unseen scenarios, qualitatively evaluate robustness to different operating conditions, and demonstrate usage as the sole input of an obstacle-avoidance controller. We additionally instantiate the approach on a different simulated scenario with complementary characteristics, to exemplify the generality of our contribution.