SLAM의 input과 output에 대해 알아보자

우선 다양한 종류의 data를 종합하여 environment를 조성한다는 것을 이해해야 한다.

 

DATA Input

 

1. LIDAR(light detection and ranging)

- Data : Distance Measurements in the form of point clouds.

- Usage : Provides highly accurate spatial information, useful for building detailed maps.

 

2. cameras

- Data : Vsual information in the form of images or video streams.

- Usage : Used in visual SLAM to extract features from the environment.

 

3. IMU(Inertial Measurement Unit)

- Data : Acceleration and roatational rates.

- Usage : Helps estimates the robot's movement and orientation.

 

4. Sonar/Ultrasonic Sensors)

- Data : Distance measurements using sound waves.

- Usage : Useful for short-range distance measurements in simpler environments.

 

5. GPS(Global Positioning System)

- Data : global position coordinates.

- Usage : Provides rough localization, typically in outdoor environments.

 

 

Data Processing

 

1. Preprocessing

- Filtering : removes noise from the sensor data.

- Calibration : adjust he sensor data to account for any biases or erros.

 

2. Feature Extraction

- Visual Features : Detecting and describing key points in images(e.g., using SIFT, SURF, ORB).

- Geometric Features : Extracting shapes and structures from point clouds(e.g., lines, planes) 

 

3. Data Association

- Matching : Identifying whether features observed at different times correspond to the same physical feature.

- Tracking : Following features over time to estimate the robot's movement

 

4. State Estimation 

- Localization : Estimating the robot's position and orientation

- Mapping: Constructing a map of the environment.

 

Data Output

 

1. Map

- Description : A representation of the environment, which can be 2D or 3D

- Format : bould be in the form of occupancy grids, point clouds or geometic shapes.

 

2. Robot Pose

- Desciption : The position and orientation of the robot within the map.

- Format : Typically represented as a 3d pose(x, y, z, roll. pitch, yaw)

 

3. Trajectory

- Description : The path that the robot has traveled over time.

- Format : A sequence of robot poses over time.

 

4. Environment Features

- Desciption : Key landmarks or features detected in the environment.

- Frmat : points or desciptors that can be used for navigation and further processing.