# LiDAR-guided object search and detection in Subterranean Environments

arXiv: https://arxiv.org/abs/2210.14997 · HTML: https://mlsanity.com/paper/2210.14997/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 14 of 14)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://mlsanity.com/paper/2006.02334/) (2020)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [LO-Net: Deep Real-time Lidar Odometry](https://mlsanity.com/paper/1904.08242/) (2019)
- [Heterogeneous Ground and Air Platforms, Homogeneous Sensing: Team CSIRO Data61's Approach to the DARPA Subterranean Challenge](https://mlsanity.com/paper/2104.09053/) (2021)
- [Team CERBERUS Wins the DARPA Subterranean Challenge: Technical Overview and Lessons Learned](https://mlsanity.com/paper/2207.04914/) (2022)
- [Visual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments](https://mlsanity.com/paper/1903.01656/) (2019)
- [Visual Place Recognition using LiDAR Intensity Information](https://mlsanity.com/paper/2103.09605/) (2021)
- [Nuclear Environments Inspection with Micro Aerial Vehicles: Algorithms and Experiments](https://mlsanity.com/paper/1903.06111/) (2019)

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

- [QuaDreamer: Controllable Panoramic Video Generation for Quadruped Robots](https://mlsanity.com/paper/2508.02512/) (2025)
- [WildOS: Open-Vocabulary Object Search in the Wild](https://mlsanity.com/paper/2602.19308/) (2026)
- [Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems](https://mlsanity.com/paper/2308.07264/) (2023)

## Abstract (arXiv)

Detecting objects of interest, such as human survivors, safety equipment, and structure access points, is critical to any search-and-rescue operation. Robots deployed for such time-sensitive efforts rely on their onboard sensors to perform their designated tasks. However, as disaster response operations are predominantly conducted under perceptually degraded conditions, commonly utilized sensors such as visual cameras and LiDARs suffer in terms of performance degradation. In response, this work presents a method that utilizes the complementary nature of vision and depth sensors to leverage multi-modal information to aid object detection at longer distances. In particular, depth and intensity values from sparse LiDAR returns are used to generate proposals for objects present in the environment. These proposals are then utilized by a Pan-Tilt-Zoom (PTZ) camera system to perform a directed search by adjusting its pose and zoom level for performing object detection and classification in difficult environments. The proposed work has been thoroughly verified using an ANYmal quadruped robot in underground settings and on datasets collected during the DARPA Subterranean Challenge finals.