Core Viewpoint - The article discusses the challenges and limitations of traditional and new methods in SLAM (Simultaneous Localization and Mapping), emphasizing the need for data-driven approaches to improve performance and reliability in real-world applications [6][12]. Group 1: Traditional Methods - Traditional SLAM methods have not significantly changed and struggle with corner cases, leading to unresolved issues [7]. - These methods do not show noticeable performance improvements as data increases, limiting their scalability [7]. Group 2: New Methods - New SLAM methods are often not generalizable, with performance heavily dependent on data distribution, unlike traditional methods which are nearly universally applicable [12]. - Current new methods fail to meet performance benchmarks on affordable hardware, requiring at least 100ms/frame for mapping and 20ms/frame for localization to be viable [12]. - Debugging new methods is challenging; issues often require additional data rather than providing clear solutions, unlike traditional methods which can identify root causes [12]. Group 3: Market Expectations - New methods typically achieve around 70-80% success in scenarios where traditional methods succeed, but they also struggle in areas where traditional methods fail, achieving only 60-70% success [13]. - End-user applications expect 100% reliability in solvable scenarios, while failures in challenging scenarios are acceptable [13]. Group 4: Future Trends - The future of SLAM is likely to be dominated by data-driven methods, as leveraging GPU capabilities to process large datasets will outperform manual tuning of noise parameters in traditional methods [13].