SLAM and mapping
Create 2D or 3D maps while localizing using LiDAR or camera data.
Autonomy
Robotics navigation, localization and perception
We develop the algorithms that enable a mobile robot to understand its environment, localize itself and reach its destination safely.
Navigation
Algorithms tailored to the robot and its environment.
Localization
Know where the robot is, even when sensor data is uncertain.
Sensor fusion
Combine odometry, IMU, GNSS, cameras and LiDAR for robust pose estimation.
Calibration
Intrinsics, extrinsics and time synchronization to make measurements consistent.
Perception
Camera, LiDAR and robotics sensor expertise.
Validation
Measure robustness rather than promise autonomy.
We define scenarios and metrics: localization error, success rate, safe distance, oscillations, travel time, CPU load and behavior under degraded conditions.
FAQ
Frequently asked questions
Camera or LiDAR: which sensor should we choose?
The choice depends on range, lighting, surfaces, computing budget and objects to detect. The two technologies are often complementary.
Can you improve unstable localization?
Yes. We analyze measurement quality, calibration, TF frames, odometry and fusion parameters before adjusting the algorithm.
Can navigation be tested in simulation?
Yes. Simulation accelerates development and regression testing, followed by parameter validation on the real robot.
Project
Does your robot need to see and move better?
Describe its environment, sensors and observed limitations.