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About Sensors & Perception
Sensors and perception systems allow robots to understand their environment, detect objects, measure distances, track movement, and make informed decisions. They are essential for autonomous navigation, manipulation, inspection, mapping, and human-robot interaction.
Robotic perception often combines multiple sensors such as cameras, LiDAR, depth cameras, IMUs, encoders, force sensors, ultrasonic sensors, and environmental sensors. These inputs are processed by software and AI algorithms to help the robot interpret the world around it.
Cameras & Vision
Learn about RGB cameras, stereo cameras, depth cameras, and computer vision systems.
LiDAR & Distance Sensors
Understand laser scanners, range sensors, ultrasonic sensors, and obstacle detection.
Motion & Position
Explore IMUs, encoders, GPS, odometry, and localisation sensors for robotics.
Perception Software
Discover how ROS, OpenCV, AI, and sensor fusion help robots interpret sensor data.
Frequently Asked Questions
Common robotics sensors include cameras, LiDAR, depth cameras, ultrasonic sensors, IMUs,
wheel encoders, force sensors, GPS modules, infrared sensors, touch sensors, microphones,
and environmental sensors.
Robot perception is the process of collecting and interpreting sensor data so that a robot
can understand its environment, detect objects, estimate position, avoid obstacles, and make
decisions.
A standard camera captures 2D image information, while a depth camera also measures distance
to objects in the scene. Depth cameras are useful for 3D perception, object detection, navigation,
and robotic manipulation.
LiDAR is used to measure distances and create accurate 2D or 3D maps of the environment.
It is commonly used for autonomous navigation, SLAM, obstacle detection, mapping, and safety systems.
An IMU, or Inertial Measurement Unit, measures acceleration, angular velocity, and sometimes
magnetic orientation. Robots use IMUs for balance, orientation estimation, stabilisation,
and navigation.
Encoders measure motor or wheel rotation. In robotics, they are commonly used for speed control,
position feedback, odometry, robotic arm joint control, and navigation.
Sensor fusion combines data from multiple sensors to produce a more accurate and reliable
understanding of the environment. For example, a robot may combine LiDAR, camera, IMU,
and encoder data for navigation.
Common sensors for obstacle avoidance include LiDAR, ultrasonic sensors, infrared sensors,
depth cameras, stereo cameras, bump sensors, and radar. The best choice depends on the robot,
environment, speed, and required accuracy.
Yes. Cameras can be used for visual navigation, visual SLAM, object recognition, line following,
landmark detection, and AI-based perception. Many systems combine cameras with other sensors
for improved reliability.
Common software tools include ROS, ROS 2, OpenCV, PCL, TensorFlow, PyTorch, NVIDIA Isaac,
RViz, Gazebo, and manufacturer-specific sensor SDKs.
Choose sensors based on your application, operating environment, required range, accuracy,
field of view, lighting conditions, update rate, interface type, power requirements, software
compatibility, and budget.
Yes. RoboSavvy can help you choose suitable cameras, LiDAR sensors, IMUs, encoders,
controllers, computing platforms, and perception software for your robotics project.
Related FAQ Categories
Need help choosing robot sensors?
Whether you are building a mobile robot, robotic arm, inspection platform, AI vision system, or autonomous navigation project, RoboSavvy can help you select the right sensors and perception tools.
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