A Review of Simultaneous Localization and Mapping Methods for Off-Road Mobile Robots
Abizov N.
8 December 2025Dr D. Pylarinos
Engineering, Technology and Applied Science Research
2025#15Issue 630219 - 30225 pp.
Simultaneous Localization and Mapping (SLAM) enable autonomous mobile robots to build a map of an unknown environment while estimating their own position within it. Unstructured terrain, dynamic environmental conditions, and sensor limitations, often encountered in off-road areas can limit the effectiveness of SLAM. This review analyzes SLAM methodologies applicable to off-road mobile robots, categorizing them into LiDAR-based, visual, multi-sensor fusion, and learning-based or semantic approaches. Each category is examined in terms of algorithmic principles, performance characteristics, and suitability for varying terrain and environmental conditions. Furthermore, to assess the performance of these categories evaluation metrics are utilized, including accuracy, drift rate, robustness, computational efficiency, and benchmarking datasets. The comparative analysis highlights trade-offs between geometric precision, adaptability, and computational demands, with multi-sensor fusion and semantic integration. Real-time operation under limited onboard computation, scalability to large unstructured terrains, resilience in GPS-denied and feature-scarce environments, and integration with autonomous navigation systems are some of the identified research gaps. The findings emphasize the need for hybrid, computation-aware SLAM frameworks and standardized off-road benchmarks to accelerate the deployment of reliable autonomous systems in challenging outdoor environments.
autonomous navigation , LiDAR-based SLAM , multi-sensor fusion , off-road mobile robots , semantic mapping , simultaneous localization and mapping , SLAM , terrain adaptability , visual SLAM
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International Engineering Technological University, Kazakhstan
International Engineering Technological University
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