SLAM Accuracy & Loop Closure Estimator

Estimate cumulative SLAM localisation drift over any trajectory length — with and without loop closure correction — and determine whether your SLAM configuration meets your accuracy requirement.

UDHY Tools / SLAM Accuracy Estimator

⚙️ Input Parameters

Trajectory Length (m)

Drift Rate (%/m)

LiDAR: 0.05–0.2% | Visual: 0.1–0.5%

Loop Closure Interval (m)

0 = loop closure disabled

Post-closure Accuracy (m)

Residual error after each closure

Required Accuracy

📊 Results

🗺️

Set your SLAM parameters to compare drift with and without loop closure correction over your full trajectory.

Final SLAM Accuracy (with loop closure)

—m

Drift without loop closure—

Max drift per segment—

Number of loop closures—

Improvement factor—

Meets accuracy requirement?—

Why SLAM Drift and Loop Closure Define AV Localisation Reliability

What This Calculates

SLAM drift accumulates linearly with distance — each scan-matching step introduces a tiny error that compounds. A 0.1% drift rate gives 0.5m of error after 500m with no loop closure. Loop closure detects revisited locations and retroactively corrects accumulated drift, reducing error by 10× or more with the same sensor and algorithm.

When to Use It

Specifying loop closure frequency in SLAM configuration
Evaluating whether pure SLAM meets your accuracy requirement
Comparing LiDAR SLAM vs visual SLAM over long trajectories
Determining when HD map overlay is necessary vs pure SLAM
Robotics competition trajectory planning

Formula

drift_no_lc = distance × (rate/100) max_drift_seg = lc_interval × (rate/100) n_closures = floor(dist / lc_interval)final_acc = max(lc_accuracy, max_drift_seg × 0.3 + lc_accuracy) improvement = drift_no_lc / final_acc

Built by Dr. Dilip Kumar Limbu · Co-Founder, MooVita · Former Principal Scientist, A*STAR · UDHY Engineering Tools

FAQs on LiDAR Point Cloud Density

1. What is a typical SLAM drift rate for LiDAR and visual SLAM?

LiDAR SLAM (LOAM, LeGO-LOAM, Cartographer) typically achieves 0.05–0.2% drift rate per metre in structured environments with good geometric features. Visual SLAM (ORB-SLAM3, VINS-Mono) typically achieves 0.1–0.5% in well-textured environments, with significantly higher rates in low-texture or variable-lighting conditions. The best published LiDAR SLAM results on KITTI benchmark achieve under 0.05% in urban driving scenarios.

2. What is loop closure in SLAM and why is it important?

Loop closure is the process of recognising a previously visited location and using that geometric constraint to retroactively correct accumulated drift through pose graph optimisation. Without loop closure, SLAM drift grows without bound — a robot mapping a large area would return to its start and find the map doesn’t close. Loop closure is the primary mechanism enabling globally consistent maps over large areas and is essential for sustained autonomous navigation.

3. How do I set the loop closure interval in my SLAM algorithm?

Loop closure interval is not directly configurable as a distance in most SLAM frameworks — it depends on how often the robot revisits previously mapped areas. To improve loop closure frequency: design navigation routes with deliberate overlap of previously mapped regions; tune loop closure detection parameters to be more sensitive; use keyframe-based SLAM where closure candidates are checked more frequently. Use this calculator to determine the maximum safe interval for your accuracy requirement, then design navigation routes accordingly.

4. What SLAM accuracy is required for lane-level AV navigation?

Lane-level autonomous navigation requires sub-30cm lateral error for a 3.5m lane, with margin for trajectory planning — corresponding to approximately 0.1m position accuracy (3σ) as a practical requirement. Achieving this with pure SLAM requires either very low drift rate sensors (≤ 0.02%/m LiDAR SLAM) with frequent loop closure, or supplementing SLAM with HD map matching. Most commercial AV deployments use LiDAR SLAM as a health monitor with HD map matching as the primary precision localisation method.

5. Can SLAM replace HD maps for AV localisation?

SLAM can provide sufficient accuracy for some AV applications but has important limitations. If the environment changes after the SLAM map was built (new barriers, seasonal vegetation, construction), the stored map may not match the current environment, causing localisation failures. HD maps are pre-built with centimetre survey accuracy and updated through dedicated mapping vehicles. The practical AV architecture uses HD maps as primary localisation reference and SLAM as a fallback for environments not covered by the HD map.

6. What is the difference between LiDAR SLAM and visual SLAM accuracy?

LiDAR SLAM generally achieves higher accuracy and robustness than visual SLAM because LiDAR provides direct metric 3D measurements independent of lighting. Visual SLAM depends on scene texture, consistent lighting, and calibration quality. In practice: LiDAR SLAM drift rate is 0.05–0.2%; visual SLAM is 0.1–0.5%. However, visual SLAM costs far less (camera vs LiDAR hardware), making it dominant for consumer robotics and indoor service robots where LiDAR cost is prohibitive.