# Sixth-Sense: Self-Supervised Learning of Spatial Awareness of Humans from a Planar Lidar

arXiv: https://arxiv.org/abs/2502.21029 · HTML: https://mlsanity.com/paper/2502.21029/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [3D Human Motion Estimation via Motion Compression and Refinement](https://mlsanity.com/paper/2008.03789/) (2020)
- [Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR](https://mlsanity.com/paper/2211.16951/) (2022)
- [Uncertainty-Aware Self-Supervised Learning of Spatial Perception Tasks](https://mlsanity.com/paper/2103.12007/) (2021)
- [Self-Supervised Prediction of the Intention to Interact with a Service Robot](https://mlsanity.com/paper/2309.07477/) (2023)
- [Learning Long-Range Perception Using Self-Supervision from Short-Range Sensors and Odometry](https://mlsanity.com/paper/1809.07207/) (2018)
- [Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues](https://mlsanity.com/paper/2404.01986/) (2024)
- [A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction](https://mlsanity.com/paper/2410.03287/) (2024)

## Abstract (arXiv)

Reliable localization of people is fundamental for service and social robots that must operate in close interaction with humans. State-of-the-art human detectors often rely on RGB-D cameras or costly 3D LiDARs. However, most commercial robots are equipped with cameras with a narrow field of view, leaving them unaware of users approaching from other directions, or inexpensive 1D LiDARs whose readings are hard to interpret. To address these limitations, we propose a self-supervised approach to detect humans and estimate their 2D pose from 1D LiDAR data, using detections from an RGB-D camera as supervision. Trained on 70 minutes of autonomously collected data, our model detects humans omnidirectionally in unseen environments with 71% precision, 80% recall, and mean absolute errors of 13cm in distance and 44deg in orientation, measured against ground truth data. Beyond raw detection accuracy, this capability is relevant for robots operating in shared public spaces, where omnidirectional awareness of nearby people is crucial for safe navigation, appropriate approach behavior, and timely human-robot interaction initiation using low-cost, privacy-preserving sensing. Deployment in two additional public environments further suggests that the approach can serve as a practical wide-FOV awareness layer for socially aware service robotics.