Challenges in SLAM: What's ahead | Sebastian Scherer | Tartan SLAM Series
Автор: AirLab
Загружено: 2021-05-27
Просмотров: 7609
Presentation by Sebastian Scherer as part of the Tartan SLAM Series.
Series overviews and links can be found on our webpage: https://theairlab.org/tartanslamseries/
This session gives an overview of the current SLAM systems, how they can be evaluated, and some of the current challenges.
Outline:
0:00 - Welcome & Intro
5:36 - SLAM Resources
6:59 - Vernacular and Background
11:07 - Challenging Environments
12:03 - Current SLAM Systems
17:06 - Evaluating SLAM Systems
22:10 - State Estimation
28:30 - Localization
35:09 - Mapping
41:15 - Summary
42:41 - Open Discussion
44:46 - How are ground truths determined?
45:58 - Best SLAM for on edge device?
47:06 - Visual place recognition in SLAM?
48:56 - Internship opportunities?
49:59 - How measure SLAM performance?
51:00 - How tie SLAM closely to specific tasks?
54:12 - Visual SLAM for day and night?
55:24 - Sensors for snow and fog?
56:16 - How deal with brownout?
57:30 - When use deep learning or traditional methods?
1:00:24 - Are learning methods just used for feature extraction?
1:03:07 - Best way to fuse sensors?
1:04:48 - Active SLAM?
1:06:33 - How contribute to TartanAir dataset?
AirLab Links:
Website: https://theairlab.org
Twitter: / airlabcmu
LinkedIn: / the-air-lab-at-carnegie-mellon-university
Facebook: / airlabcmu
Medium: / airlabcmu
RPL Links:
Website: https://rpl.ri.cmu.edu/
Twitter: / rpl_cmu
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