# Semantic Exploration and Dense Mapping of Complex Environments using Ground Robot with Panoramic LiDAR-Camera Fusion

arXiv: https://arxiv.org/abs/2505.22880 · HTML: https://mlsanity.com/paper/2505.22880/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 21 of 21)

- [VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation](https://mlsanity.com/paper/2312.03275/) (2023)
- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [CubeSLAM: Monocular 3D Object SLAM](https://mlsanity.com/paper/1806.00557/) (2018)
- [SuMa++: Efficient LiDAR-based Semantic SLAM](https://mlsanity.com/paper/2105.11320/) (2021)
- [QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM](https://mlsanity.com/paper/1804.04011/) (2018)
- [FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning](https://mlsanity.com/paper/2010.11561/) (2020)
- [SEEK: Semantic Reasoning for Object Goal Navigation in Real World Inspection Tasks](https://mlsanity.com/paper/2405.09822/) (2024)
- [RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System](https://mlsanity.com/paper/2209.08533/) (2022)
- [SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation](https://mlsanity.com/paper/2406.17249/) (2024)
- [FALCON: Fast Autonomous Aerial Exploration using Coverage Path Guidance](https://mlsanity.com/paper/2407.00577/) (2024)
- [Finding Things in the Unknown: Semantic Object-Centric Exploration with an MAV](https://mlsanity.com/paper/2302.14569/) (2023)
- [3D Active Metric-Semantic SLAM](https://mlsanity.com/paper/2309.06950/) (2023)
- [A vision-based autonomous UAV inspection framework for unknown tunnel construction sites with dynamic obstacles](https://mlsanity.com/paper/2301.08422/) (2023)
- [A Tree-based Next-best-trajectory Method for 3D UAV Exploration](https://mlsanity.com/paper/2407.04386/) (2024)
- [Heuristic-based Incremental Probabilistic Roadmap for Efficient UAV Exploration in Dynamic Environments](https://mlsanity.com/paper/2309.09121/) (2023)
- [Star-Searcher: A Complete and Efficient Aerial System for Autonomous Target Search in Complex Unknown Environments](https://mlsanity.com/paper/2402.16348/) (2024)
- [Real-Time Volumetric-Semantic Exploration and Mapping: An Uncertainty-Aware Approach](https://mlsanity.com/paper/2109.01474/) (2021)
- [EPIC: A Lightweight LiDAR-Based UAV Exploration Framework for Large-Scale Scenarios](https://mlsanity.com/paper/2410.14203/) (2024)
- [Safe-Construct: Redefining Construction Safety Violation Recognition as 3D Multi-View Engagement Task](https://mlsanity.com/paper/2504.10880/) (2025)
- [Early Recall, Late Precision: Multi-Robot Semantic Object Mapping under Operational Constraints in Perceptually-Degraded Environments](https://mlsanity.com/paper/2206.10062/) (2022)

## Abstract (arXiv)

This paper presents a system for autonomous semantic exploration and dense semantic target mapping of a complex unknown environment using a ground robot equipped with a LiDAR-panoramic camera suite. Existing approaches often struggle to balance collecting high-quality observations from multiple view angles and avoiding unnecessary repetitive traversal. To fill this gap, we propose a complete system combining mapping and planning. We first redefine the task as completing both geometric coverage and semantic viewpoint observation. We then manage semantic and geometric viewpoints separately and propose a novel Priority-driven Decoupled Local Sampler to generate local viewpoint sets. This enables explicit multi-view semantic inspection and voxel coverage without unnecessary repetition. Building on this, we develop a hierarchical planner to ensure efficient global coverage. In addition, we propose a Safe Aggressive Exploration State Machine, which allows aggressive exploration behavior while ensuring the robot's safety. Our system includes a plug-and-play semantic target mapping module that integrates seamlessly with state-of-the-art SLAM algorithms for pointcloud-level dense semantic target mapping. We validate our approach through extensive experiments in both realistic simulations and complex real-world environments. Simulation results show that our planner achieves faster exploration and shorter travel distances while guaranteeing a specified number of multi-view inspections. Real-world experiments further confirm the system's effectiveness in achieving accurate dense semantic object mapping of unstructured environments.