(plan) The Deep SLAM Era: What Should Be Learnt?

Giseop Kim

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Jun 12, 2020

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NOTE

• Contents were selected first, I’m under making summaries of them, published later.
• The below contents mainly consider ego-motion estimation or metric localization, not the place recognition.
• For place recognition, the deep learning-oriented scheme has been known to be a good choice because it effectively summarizes raw and complex sensor data (e.g., NetVLAD).

1. Early works: try to end-to-end regression and make it better

• 15 CVPR PoseNet
• 17 ICRA DeepVO, 18 ICRA UnDeepVO
• 17 CVPR (PoseNet v2) Geometric loss functions for camera pose regression with deep learning
• 18 RAL VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry
• 18 CVPR (MapNet) Geometry-aware learning of maps for camera localization

2. Transition: not direct regress …

• 18 ECCV Relocnet: Continuous metric learning relocalisation using neural nets

3. Lesson: No more deep-only dream

• 19 CVPR Understanding the Limitations of CNN-based Absolute Camera Pose Regression

4. Current Trend: combine the traditional well-defined geometrical methods and SLAM pipeline

• 17 CVPR CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
• 18 ECCV Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry
• 20 ICRA Visual Odometry Revisited: What Should Be Learnt?
• 20 ICRA To Learn or Not to Learn: Visual Localization from Essential Matrices
• 20 RAL (ICRA) GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization
• 20 RAL (ICRA) DeepFactors: Real-Time Probabilistic Dense Monocular SLAM
• 20 CVPR D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry