If your robot will navigate a busy industrial floor with moving people, forklifts, and shifting light, the safest qualitative bet is LiDAR-based SLAM for robustness or a multi-modal fusion system for maximum accuracy and resilience. If your priority is a low hardware budget on a resource-constrained platform in a controlled indoor space, camera-based Visual SLAM is the more cost-effective category. That is the core trade-off robotics engineers face, and no single category wins on all three axes of accuracy, computational cost, and environmental robustness.
This comparison is deliberately qualitative: it covers broad algorithm categories rather than benchmarking specific implementations such as Cartographer against ORB-SLAM3, and it draws on established principles from robotics research rather than new performance testing. The scope is indoor, industrial-style autonomous mobile robots.
LiDAR-based SLAM: Precision and Robustness
LiDAR-based SLAM is the category most often chosen when consistent, accurate localization matters more than sensor cost. Systems in this family, such as Cartographer and Hector SLAM, build maps from laser range data and, according to a Thinkrobotics technical guide for mobile-robot builders, "provide more consistent performance across varying lighting conditions" than camera-based approaches. Because the sensor measures distances directly with its own emitted light, illumination changes in the environment do not degrade the input signal the way they degrade camera images.
The same guide reports that LiDAR algorithms such as GMapping and Hector SLAM require moderate computational resources, and that Cartographer uses global optimization to minimize the impact of dynamic objects on mapping accuracy. That robustness in changing scenes, combined with strong mapping accuracy, is why the LiDAR category is generally preferred for large-scale or dynamic environments where a wrong localization could mean a collision or a stalled mission.
The trade-offs are on the cost side of the ledger. LiDAR sensors themselves are more expensive hardware than cameras, and a CEUR-WS research analysis of SLAM algorithm families, covering LIDAR SLAM alongside VSLAM and IMU SLAM, treats computational complexity and scalability as central evaluation metrics precisely because heavy sensor data drives processing load. Even so, for most indoor industrial applications, the guide finds that 2D LiDAR sensors paired with algorithms like Cartographer offer "excellent cost-performance balance," delivering accurate mapping at reasonable computational and hardware cost compared to multi-sensor configurations.
