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"source2": "file list", "unified_diff": "@@ -1,3 +1,3 @@\n -rw-r--r-- 0 0 0 4 2024-02-28 09:35:01.000000 debian-binary\n--rw-r--r-- 0 0 0 162172 2024-02-28 09:35:01.000000 control.tar.xz\n--rw-r--r-- 0 0 0 208790488 2024-02-28 09:35:01.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 162180 2024-02-28 09:35:01.000000 control.tar.xz\n+-rw-r--r-- 0 0 0 208792356 2024-02-28 09:35:01.000000 data.tar.xz\n"}, {"source1": "control.tar.xz", "source2": "control.tar.xz", "unified_diff": null, "details": [{"source1": "control.tar", "source2": "control.tar", "unified_diff": null, "details": [{"source1": "./control", "source2": "./control", "unified_diff": "@@ -1,13 +1,13 @@\n Package: libg2o-doc\n Source: g2o\n Version: 0~20230806-4.1\n Architecture: all\n Maintainer: Debian Science Maintainers \n-Installed-Size: 313544\n+Installed-Size: 313543\n Section: doc\n Priority: optional\n Homepage: http://www.g2o.org\n Description: C++ framework for optimizing graph-based nonlinear error functions\n A wide range of problems in robotics as well as in computer-vision involve the\n minimization of a non-linear error function that can be represented as a graph.\n Typical instances are simultaneous localization and mapping (SLAM) or bundle\n"}, {"source1": "./md5sums", "source2": "./md5sums", "unified_diff": null, "details": [{"source1": "./md5sums", "source2": "./md5sums", "comments": ["Files differ"], "unified_diff": null}]}]}, {"source1": "xz --list", "source2": "xz --list", "unified_diff": "@@ -1,13 +1,13 @@\n Streams: 1\n Blocks: 1\n- Compressed size: 158.4 KiB (162172 B)\n+ Compressed size: 158.4 KiB (162180 B)\n Uncompressed size: 770.0 KiB (788480 B)\n Ratio: 0.206\n Check: CRC64\n Stream Padding: 0 B\n Streams:\n Stream Blocks CompOffset UncompOffset CompSize UncompSize Ratio Check Padding\n- 1 1 0 0 162172 788480 0.206 CRC64 0\n+ 1 1 0 0 162180 788480 0.206 CRC64 0\n Blocks:\n Stream Block CompOffset UncompOffset TotalSize UncompSize Ratio Check\n- 1 1 12 0 162136 788480 0.206 CRC64\n+ 1 1 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can be embedded as a graph or in a hyper-graph. A hyper-graph is an extension\n of a graph where an edge can connect multiple nodes and not only two. Several problems in robotics and\n in computer vision require to find the optimum of an error function with respect of a set of parameters.\n Examples include, popular applications like SLAM and Bundle adjustment.\n In the literature, many approaches have been proposed to address this class of problems. 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