Chapter 10: Visual SLAM & Localization
Understand how autonomous systems simultaneously build maps and track their position using cameras and IMUs. From classical feature-based methods like ORB-SLAM to modern deep learning approaches, master the complete Visual SLAM pipeline.
Chapter Overview
Visual Simultaneous Localization and Mapping (SLAM) is the problem of building a map of an unknown environment while simultaneously tracking the camera's position within that map. This chicken-and-egg problem --- you need a map to localize, but you need to know your location to build a map --- is solved through iterative optimization that refines both the map and trajectory together.
SLAM systems are divided into a front-end (real-time tracking, feature matching, local optimization) and a back-end (global optimization, loop closure, map management). The front-end processes each new frame in real time, estimating the camera motion relative to the previous frame. The back-end runs asynchronously, detecting when the camera revisits a previously mapped area (loop closure) and correcting accumulated drift through global optimization.
The chapter covers the full spectrum of visual SLAM: feature-based methods (ORB-SLAM) that track sparse keypoints, direct methods (LSD-SLAM) that operate on raw pixel intensities, visual-inertial approaches (VINS-Mono) that fuse camera with IMU data, and modern deep learning methods that replace hand-crafted features with learned representations.
Key topics include:
- Feature-based SLAM: ORB features, tracking, local mapping, and loop closure
- Direct methods: Photometric alignment without feature extraction
- Visual-inertial odometry: Tight fusion of camera and inertial measurement
- Deep SLAM: Learned features (SuperPoint, SuperGlue) for robust matching
- Relocalization: Recognizing previously visited places for drift correction
- Map optimization: Pose graph optimization and bundle adjustment
Chapter Roadmap
Click any topic to jump in
SLAM Overview
Front-end tracking, back-end optimization, and keyframe selection — the core SLAM architecture.
Two paradigms for front-end motion estimation
ORB-SLAM Pipeline
Feature-based SLAM with ORB descriptors, covisibility graphs, and bundle adjustment.
LSD-SLAM & Direct Methods
Photometric alignment on raw pixel intensities without feature extraction.
Improving robustness with additional sensors and deep learning
Visual-Inertial Odometry
Tight fusion of camera and IMU measurements via preintegration.
Deep SLAM Features
SuperPoint and SuperGlue replace hand-crafted features with learned representations.
Closing the loop for accurate long-range mapping
Relocalization
NetVLAD and HLoc for recognizing previously visited places and correcting drift.
Loop Closure & Map Optimization
Pose graph optimization and robust verification for globally consistent maps.
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