SLAM

Visual SLAM

Uses cameras to construct a map of the environment. Does feature matching, see ORB-SLAM

Tesla kind of does SLAM by creating a bird-eyes view from multiple camera views.

Other resources:

• https://www.kudan.io/blog/camera-basics-visual-slam/
• SLAM Textbook
• https://faculty.cc.gatech.edu/~afb/classes/CS7495-Fall2014/presentations/visual_slam.pdf

You basically try to figure out where the features align.

Implementation

• https://github.com/openxrlab/xrslam
• This guy’s student implementation of SLAM might be good https://github.com/MiguelAngelCalveraUnizar/Mini-SLAM_student

Concepts

• Feature Point
• Camera Calibration
• Camera Motion Estimation

Classical Visual SLAM Stack

Typical Visual SLAM Workflow

A typical visual SLAM workflow includes the following steps:

1. Sensor data acquisition. (cameras, and optionally motor encoders, IMUs, etc).
2. Visual Odometry (frontend): VO’s task is to estimate the camera movement between adjacent frames (ego-motion) and generate a rough local map.
3. Backend filtering/optimization (backend). Receives poses from VO and loop closing, and then applies optimization to generate a fully optimized trajectory and map through Bundle Adjustment
4. Loop Closing. Determines whether the robot has returned to its previous position in order to reduce the accumulated drift. If a loop is detected, it will provide information to the backend for further optimization.
5. Reconstruction (optional). It constructs a task-specific map based on the estimated camera trajectory.

frontend $\to$ more relevant to computer vision topics (image feature extraction and matching) backend $\to$ state estimation research area

SLAM Formalization

See SLAM Formalization.

Other

I think Sachin showed me this image instead

Related

• State Estimation