Simultaneous Localization and Mapping with Lidar and Visual Data
Summary
Simultaneous Localization and Mapping with Lidar and visual data combines active range sensing and passive imaging to enable autonomous agents to navigate and build detailed environmental models in real time. By fusing three-dimensional point clouds from Lidar with two-dimensional visual cues, modern SLAM frameworks exploit complementary strengths: Lidar offers accurate distance measurement and robust performance under varying illumination, while cameras capture dense texture and semantic information. Typical systems consist of a front-end that performs sensor odometry and feature extraction, and a back-end that executes pose graph optimisation and loop closure detection. Sensor fusion strategies range from tightly coupled algorithms, which jointly optimise measurements in a single filtering or smoothing framework, to loosely coupled pipelines that exchange odometry updates and corrections. Advances in calibration, dynamic object handling and uncertainty modelling have broadened applicability to self-driving cars, aerial robots and mobile service platforms. The integration of machine learning in feature selection and data association promises further improvements in robustness and scalability, reducing drift in challenging indoor, outdoor and subterranean environments.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Simultaneous Localization and Mapping with Lidar and Visual Data publication trend
The graph below shows the total number of articles in simultaneous localization and mapping with lidar and visual data across all publications each year (not limited to Nature Index journals).
Technical terms
Simultaneous Localization and Mapping (SLAM): The process by which a mobile agent concurrently estimates its pose and constructs a map of the environment.
LiDAR: An active remote-sensing technology that measures distances by timing laser pulses to generate three-dimensional point clouds.
Visual odometry: Estimation of relative camera motion based on sequential image feature tracking and pose estimation.
Loop closure: The detection and correction of re-visited places to eliminate accumulated drift in the pose graph.
Point cloud: A set of spatial points in three dimensions representing the surface geometry of the environment.
Graph optimisation: A back-end process that refines pose and landmark estimates by minimising error over a network of spatial constraints.
References
- A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping. Sensors (2020).
- A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization. Remote Sensing (2021).
- DV-LOAM: Direct Visual LiDAR Odometry and Mapping. Remote Sensing (2021).
- SLAM Overview: From Single Sensor to Heterogeneous Fusion. Remote Sensing (2022).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.