# Enhancing Autonomous Navigation by Imaging Hidden Objects using Single-Photon LiDAR

arXiv: https://arxiv.org/abs/2410.03555 · HTML: https://mlsanity.com/paper/2410.03555/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [Virtual Wave Optics for Non-Line-of-Sight Imaging](https://mlsanity.com/paper/1810.07535/) (2018)
- [Optical Non-Line-of-Sight Physics-based 3D Human Pose Estimation](https://mlsanity.com/paper/2003.14414/) (2020)
- [Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control](https://mlsanity.com/paper/2011.00032/) (2020)
- [Towards 3D Vision with Low-Cost Single-Photon Cameras](https://mlsanity.com/paper/2403.17801/) (2024)
- [Keyhole Imaging: Non-Line-of-Sight Imaging and Tracking of Moving Objects Along a Single Optical Path](https://mlsanity.com/paper/1912.06727/) (2019)
- [PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar](https://mlsanity.com/paper/2312.14239/) (2023)
- [Long-range depth imaging using a single-photon detector array and non-local data fusion](https://mlsanity.com/paper/1812.04943/) (2018)
- [Role of Transients in Two-Bounce Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2304.01308/) (2023)
- [Detection and Mapping of Specular Surfaces Using Multibounce Lidar Returns](https://mlsanity.com/paper/2209.03336/) (2022)
- [NMPC trajectory planner for urban autonomous driving](https://mlsanity.com/paper/2105.04034/) (2021)
- [A software toolkit and hardware platform for investigating and comparing robot autonomy algorithms in simulation and reality](https://mlsanity.com/paper/2206.06537/) (2022)
- [Using a Distance Sensor to Detect Deviations in a Planar Surface](https://mlsanity.com/paper/2408.03838/) (2024)
- [Enhanced Robot Motion Block of A-star Algorithm for Robotic Path Planning](https://mlsanity.com/paper/2312.15738/) (2023)

## Built on by (most cited first, 8 of 8)

- [Recovering Parametric Scenes from Very Few Time-of-Flight Pixels](https://mlsanity.com/paper/2509.16132/) (2025)
- [TransiT: Transient Transformer for Non-line-of-sight Videography](https://mlsanity.com/paper/2503.11328/) (2025)
- [Toward Robust Single-Photon Perception for Robots: A Condition-Aware Active Learning Approach](https://mlsanity.com/paper/2505.04376/) (2025)
- [Task-Driven Implicit Representations for Automated Design of LiDAR Systems](https://mlsanity.com/paper/2505.22344/) (2025)
- [SuperEx: Enhancing Indoor Mapping and Exploration using Non-Line-of-Sight Perception](https://mlsanity.com/paper/2510.10506/) (2025)
- [Spatial Calibration of Diffuse LiDARs](https://mlsanity.com/paper/2603.06531/) (2026)
- [DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs](https://mlsanity.com/paper/2604.16201/) (2026)
- [SP-TransientBench: A Real-Captured Single Photon Perception Benchmark](https://mlsanity.com/paper/2606.18952/) (2026)

## Abstract (arXiv)

Robust autonomous navigation in environments with limited visibility remains a critical challenge in robotics. We present a novel approach that leverages Non-Line-of-Sight (NLOS) sensing using single-photon LiDAR to improve visibility and enhance autonomous navigation. Our method enables mobile robots to "see around corners" by utilizing multi-bounce light information, effectively expanding their perceptual range without additional infrastructure. We propose a three-module pipeline: (1) Sensing, which captures multi-bounce histograms using SPAD-based LiDAR; (2) Perception, which estimates occupancy maps of hidden regions from these histograms using a convolutional neural network; and (3) Control, which allows a robot to follow safe paths based on the estimated occupancy. We evaluate our approach through simulations and real-world experiments on a mobile robot navigating an L-shaped corridor with hidden obstacles. Our work represents the first experimental demonstration of NLOS imaging for autonomous navigation, paving the way for safer and more efficient robotic systems operating in complex environments. We also contribute a novel dynamics-integrated transient rendering framework for simulating NLOS scenarios, facilitating future research in this domain.