Real-World Sensor Data
Intelligently Expanded

Maati mimics sensor behavior in real-world edge case scenarios. Access realistic LiDAR and camera readings that replicate failures physics simulations miss—giving startups production-grade training data without the cost of real-world data collection.

Note: The images below illustrate real-world scenarios. Maati generates the underlying sensor data (LiDAR point clouds, range readings, timestamps) that would be captured in these conditions—not the images themselves.

SIDE-BY-SIDE COMPARISON

CASE 01

Puddle Reflection Artifact

PHYSICS SIMULATION

lidar_points 10000

range_m 15.0

dropouts 0

ghost_returns 0

MAATI REAL-WORLD DATA

lidar_points 8247

range_m 2.5

dropouts 0

ghost_returns 1

Reality: Water on pavement creates false LiDAR return at 2.5m (building reflection in puddle). Physics sims model dry surfaces only. Maati provides sensor readings showing how wet surfaces produce phantom obstacles, enabling models to learn reflection filtering without deploying hardware in rain.

CASE 02

Direct Solar Saturation

PHYSICS SIMULATION

lidar_points 10000

range_m 12.0

dropouts 0

duration_sec cont.

MAATI REAL-WORLD DATA

lidar_points 0

range_m N/A

dropouts 1

duration_sec 1.8

Reality: Direct sunlight at 4:47pm causes complete sensor saturation for 1.8 seconds. Physics sims use uniform lighting. Maati mimics time-of-day sensor dropout patterns, providing training data with temporal gaps and recovery sequences—critical for deployment robustness without waiting for specific sun angles.

CASE 03

Chrome Specular Reflection

PHYSICS SIMULATION

lidar_points 10000

range_m 3.2

range_error 0.0

collision no

MAATI REAL-WORLD DATA

lidar_points 10000

range_m 0.4

range_error +2.8m

collision yes

Reality: Chrome bumper reflects LiDAR instantly, robot calculates 0.4m when car is 3.2m away. Physics sims assume diffuse materials. Maati replicates specular reflection behavior in sensor data, providing range miscalculation examples with collision labels—training models on material properties without physical crashes.

CASE 04

Lightweight Debris Classification

PHYSICS SIMULATION

lidar_points 10000

range_m 10.0

object_type rigid

velocity 0.0

MAATI REAL-WORLD DATA

lidar_points 10000

range_m 2.8

object_type deform

velocity 3.2

Reality: Plastic bag triggers obstacle detection. Dynamic debris (bags, paper, leaves) move unpredictably. Physics sims model rigid static objects. Maati provides sensor data showing deformable object behavior and velocity patterns, enabling classification training (critical vs benign) without manual debris collection and labeling.

Real-World Sensor Data Intelligently Expanded

Access Real-World Training Data

Real-World Sensor Data
Intelligently Expanded