Invited Talk: Jose Neira: The Chimera of Robust Place Recognition
In this talk I will discuss the history of the place recognition, or loop closing problem in SLAM. I will also present some of the most recent algorithms and results of our group in this field, and I will also explain why hoping for a fool-proof place recognition algorithm is a chimera, and SLAM systems should instead accommodate for possible failures in place recognition.
Deep Learning for Place Recognition & What to do if Place Recognition Fails
Invited Talk: Jose M.M. Montiel: Visual Place recognition in ORBSLAM
The talk focuses on the ORBSLAM a bag of words place recognizer built on DBoW2 with ORB. ORB are binary features invariant to rotation and scale, resulting in a very fast recognizer with good invariance to viewpoint. The system has been designed targeting a tight integration of the place recognition at the core the visual SLAM architecture. Thanks to this tight integration the system achieves a reliable relocation and loop closure capabilities that warrants the full system robust performance in realistic operational environments.
|16:00||Fei Han et al. – Life-Long Place Recognition by Shared Representative Appearance Learning|
Muneeb Shahid, Tayyab Naseer, Wolfram Burgard – DTLC: Deeply Trained Loop Closure Detections for Lifelong Visual SLAM
Charbel Azzi et al. – Global Descriptors Reduce the Image-Based Localization Search Space
|16:45||Stephanie Lowry and Henrik Andreasson – Visual place recognition techniques for pose estimation in changing environments|
|17:00||Concluding Remarks and Discussion|