IMU Dead-Reckoning Drift Calculator

Q: 1. What causes IMU drift in autonomous vehicles and robots?

Camera FOV is calculated using the formula: FOV = 2 × arctan(sensor_dimension / (2 × focal_length)). Apply this separately to sensor width and height for horizontal and vertical FOV. For example, a 6.4mm wide sensor with a 4mm focal length gives: HFOV = 2 × arctan(6.4 / 8) = 2 × arctan(0.8) ≈ 77°. This calculator handles the trigonometry automatically — enter your sensor dimensions and focal length for the result.

Q: 2. What camera FOV is recommended for autonomous vehicle front cameras?

Most AV front camera stacks use a combination of FOVs to cover different detection tasks. A wide-angle camera (90–120° horizontal FOV) provides near-field coverage and pedestrian detection at intersection crossings. A medium camera (50–70° HFOV) covers the primary driving lane and traffic signs at 30–60m. A narrow telephoto (20–35° HFOV) covers long-range detection at 100m+. Waymo's Jaguar platform, for example, uses 29 cameras with overlapping FOVs for full 360° coverage with multiple resolution zones.

Q: 3. What is the difference between horizontal and vertical FOV in cameras?

Horizontal FOV describes the angular width of the camera's view — relevant for lane coverage and side detection. Vertical FOV describes the angular height — relevant for detecting objects that span a height range (pedestrians, cyclists, traffic lights). Most image sensors are wider than they are tall (matching standard video aspect ratios), so horizontal FOV is typically larger than vertical FOV for the same focal length.

Q: 4. How does focal length affect camera FOV?

Shorter focal lengths produce wider FOV (more scene captured, less detail per pixel). Longer focal lengths produce narrower FOV (less scene captured, more detail per pixel). For a fixed sensor size, doubling the focal length approximately halves the angular FOV. For AV applications: wide-angle lenses (short focal length) are used for near-field coverage and intersection awareness; telephoto lenses (long focal length) are used for long-range traffic sign and obstacle detection.

Q: 5. Why does ground coverage at range matter for AV sensor design?

Ground coverage at range is the practical metric that determines whether your camera can see what it needs to see at your required detection distance. An AV camera with 90° HFOV at 50m covers approximately 100m of ground width — far wider than needed for lane detection, suggesting a narrower lens may provide better range performance for the same resolution. This calculator lets you evaluate the actual coverage at any range, so you can verify your lens selection against your ODD's detection requirements rather than working from abstract FOV angles.

Q: 6. How do I use FOV to calculate sensor overlap between adjacent cameras?

Sensor overlap ensures no blind spots between adjacent cameras in a multi-camera array. To calculate overlap: compute the ground coverage width of each camera at the distance where overlap is required, then calculate the horizontal displacement between camera mounting positions. Overlap at range = (individual camera ground width) − (camera separation). A minimum 10–15% overlap is typically required for reliable object handoff between cameras in a perception pipeline.

Quantify how much position and heading error accumulates from IMU-only dead-reckoning over time — and determine the maximum correction interval needed to maintain your accuracy requirement.

UDHY Tools / IMU Drift Calculator

⚙️ Input Parameters

Accelerometer Bias (mg)

Consumer MEMS: 0.5–5mg | Tactical: 0.01–0.1mg

Gyroscope Bias (°/hr)

Consumer MEMS: 1–10°/hr | Navigation: 0.1°/hr

Integration Time (s)

Seconds since last external correction

Vehicle Speed (m/s)

30 km/h ≈ 8.3 | 60 km/h ≈ 16.7

📊 Results

📐

Enter IMU specifications to see how much position error accumulates without an external correction source.

Total Position Drift

—m

Position error (accel bias)—

Velocity error (accel bias)—

Heading drift (gyro bias)—

Lateral error from heading—

Max interval for <0.1m—

Why IMU Drift Matters for AV Localisation

What This Calculates

IMU errors accumulate through double integration — a small constant accelerometer bias grows as ½at² (quadratic with time). A 0.5mg bias produces 0.5cm error after 1s, but 48cm after 10s. Gyroscope bias creates heading error that then generates lateral position error proportional to distance × sin(heading error).

When to Use It

Specifying minimum IMU grade for a localisation requirement
Setting GPS correction frequency for sub-10cm accuracy
Evaluating MEMS IMU adequacy for tunnel/urban canyon operation
Sizing EKF update rate in a localisation filter
Complementary to the GPS-Denied Drift Estimator tool

Formula

a_bias = mg × 0.001 × 9.81 m/s² pos_err = ½ × a_bias × t² vel_err = a_bias × t head_err = gyro_bias × (t/3600)lat_err = dist × sin(head_err_rad) total = √(pos_err² + lat_err²)

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

FAQs on IMU Dead-Reckoning Drift

1. What causes IMU drift in autonomous vehicles and robots?

Camera FOV is calculated using the formula: FOV = 2 × arctan(sensor_dimension / (2 × focal_length)). Apply this separately to sensor width and height for horizontal and vertical FOV. For example, a 6.4mm wide sensor with a 4mm focal length gives: HFOV = 2 × arctan(6.4 / 8) = 2 × arctan(0.8) ≈ 77°. This calculator handles the trigonometry automatically — enter your sensor dimensions and focal length for the result.

2. What camera FOV is recommended for autonomous vehicle front cameras?

Most AV front camera stacks use a combination of FOVs to cover different detection tasks. A wide-angle camera (90–120° horizontal FOV) provides near-field coverage and pedestrian detection at intersection crossings. A medium camera (50–70° HFOV) covers the primary driving lane and traffic signs at 30–60m. A narrow telephoto (20–35° HFOV) covers long-range detection at 100m+. Waymo’s Jaguar platform, for example, uses 29 cameras with overlapping FOVs for full 360° coverage with multiple resolution zones.

3. What is the difference between horizontal and vertical FOV in cameras?

Horizontal FOV describes the angular width of the camera’s view — relevant for lane coverage and side detection. Vertical FOV describes the angular height — relevant for detecting objects that span a height range (pedestrians, cyclists, traffic lights). Most image sensors are wider than they are tall (matching standard video aspect ratios), so horizontal FOV is typically larger than vertical FOV for the same focal length.

4. How does focal length affect camera FOV?

Shorter focal lengths produce wider FOV (more scene captured, less detail per pixel). Longer focal lengths produce narrower FOV (less scene captured, more detail per pixel). For a fixed sensor size, doubling the focal length approximately halves the angular FOV. For AV applications: wide-angle lenses (short focal length) are used for near-field coverage and intersection awareness; telephoto lenses (long focal length) are used for long-range traffic sign and obstacle detection.

5. Why does ground coverage at range matter for AV sensor design?

Ground coverage at range is the practical metric that determines whether your camera can see what it needs to see at your required detection distance. An AV camera with 90° HFOV at 50m covers approximately 100m of ground width — far wider than needed for lane detection, suggesting a narrower lens may provide better range performance for the same resolution. This calculator lets you evaluate the actual coverage at any range, so you can verify your lens selection against your ODD’s detection requirements rather than working from abstract FOV angles.

6. How do I use FOV to calculate sensor overlap between adjacent cameras?

Sensor overlap ensures no blind spots between adjacent cameras in a multi-camera array. To calculate overlap: compute the ground coverage width of each camera at the distance where overlap is required, then calculate the horizontal displacement between camera mounting positions. Overlap at range = (individual camera ground width) − (camera separation). A minimum 10–15% overlap is typically required for reliable object handoff between cameras in a perception pipeline.