Cartographer vs Hector SLAM

Google Cartographer excels at producing globally consistent, large-scale maps because it fuses LiDAR, IMU, and odometry data into a rigorous pose-graph optimization framework. Its standout feature is robust loop closure detection using a branch-and-bound scan matching algorithm, which corrects accumulated drift in real-time. For example, in a 50,000 sq. ft. warehouse, Cartographer can maintain a sub-5 cm resolution map with drift reduced to less than 0.1% of the distance traveled, making it the de facto standard for long-term autonomy in logistics AMRs.

Hector SLAM takes a fundamentally different approach by requiring only a high-rate LiDAR, such as a Hokuyo or RPLIDAR, without needing odometry. It achieves real-time pose estimation by matching consecutive laser scans to an occupancy grid map using a Gauss-Newton minimization technique. This results in a lightweight, computationally efficient system that can run on a Raspberry Pi 4 with a CPU load of under 30%. The trade-off is that without odometry or loop closure, it is susceptible to drift in large, feature-sparse environments, making it best suited for rapid mapping of small indoor spaces or handheld 3D scanners.

The key trade-off: If your priority is global map consistency and long-duration autonomy for a warehouse AMR, choose Cartographer. If you prioritize a minimal sensor payload, low computational overhead, and rapid deployment on a resource-constrained ground robot for a single room, choose Hector SLAM.

Cartographer excels at producing globally consistent, production-grade maps because of its robust loop closure detection and sparse pose graph optimization. For example, in large-scale warehouse environments exceeding 10,000 sqm, Cartographer's ability to recognize previously visited locations eliminates the odometric drift that cripples dead-reckoning systems. This results in sub-5cm map accuracy even after extended runs, making it the standard for commercial Autonomous Mobile Robots (AMRs) that must operate reliably for years without map degradation.

Hector SLAM takes a fundamentally different approach by coupling 2D pose estimation tightly with a high-rate LiDAR scan matcher, requiring no odometry source. This results in a remarkably low computational overhead, often running smoothly on a single-board computer like a Raspberry Pi 4 with a sub-$100 RPLIDAR. The trade-off is the absence of a global backend: without loop closure, Hector SLAM accumulates rotational drift, typically 1-2 degrees per minute, which makes it unsuitable for mapping large, interconnected spaces but perfect for rapid, local exploration.

The key trade-off: If your priority is long-term autonomy and a globally consistent map for a warehouse AMR, choose Cartographer. If you prioritize rapid deployment on a resource-constrained ground robot for quick, local area mapping without the complexity of odometry calibration, choose Hector SLAM.