Mobile Robot Localization and Navigation Techniques
Summary
Mobile robot localisation and navigation constitute the foundational processes by which autonomous platforms determine their position within an environment and plan safe, efficient routes to reach desired destinations. Localisation algorithms estimate the robot’s pose by integrating data from sensors such as LiDAR, cameras and inertial measurement units (IMUs), often within a probabilistic framework to quantify uncertainty. Simultaneous Localisation and Mapping (SLAM) extends this capability by building and refining a map of an unknown environment while localising the robot within it. Navigation leverages these spatial estimates to generate collision-free paths, taking into account dynamic obstacles, kinematic constraints and real-time replanning. Advances in sensor fusion, machine learning and optimisation have enhanced robustness in cluttered or GPS-denied settings, enabling applications from warehouse automation and planetary exploration to last-mile delivery and multi-robot coordination. Recent trends emphasise lightweight computation, resilience against sensory noise and strategies for collaboration across robot teams, underscoring the global importance of dependable autonomy in both indoor and outdoor domains.
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Mobile Robot Localization and Navigation Techniques publication trend
The graph below shows the total number of articles in mobile robot localization and navigation techniques across all publications each year (not limited to Nature Index journals).
Technical terms
Localisation: The process of estimating a robot’s position and orientation within a known or unknown environment.
Simultaneous Localisation and Mapping (SLAM): A computational methodology whereby a robot constructs a map of an unknown environment while concurrently determining its own pose within that map.
Extended Kalman Filter (EKF): A recursive algorithm that linearises non-linear motion and observation models to fuse sensor data and produce optimal state estimates with associated uncertainty.
Particle Filter (Adaptive Monte Carlo Localization): A probabilistic technique that represents the robot’s pose distribution as a set of weighted hypotheses (particles) and updates them based on motion and sensor measurements.
Sensor Fusion: The integration of data from multiple sensors (e.g. LiDAR, IMU, cameras) to produce more accurate and robust state estimates than any single modality alone.
Path Planning: The computation of a collision-free trajectory from an initial pose to a goal pose, respecting the robot’s kinematic and dynamic constraints.
References
- Enhancing Robot Localization Accuracy through Neural Networks and Boosting Techniques. Journal of Robotics Spectrum (2023).
- Improved LiDAR Localization Method for Mobile Robots Based on Multi-Sensing. Remote Sensing (2022).
- A survey of autonomous robots and multi-robot navigation: Perception, planning and collaboration. Biomimetic Intelligence and Robotics (2025).
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