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"unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -180,15 +180,15 @@\n -rw-r--r--   0 root         (0) root         (0)     2075 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/tutorial_slam2d.cpp.gz\n -rw-r--r--   0 root         (0) root         (0)     2021 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/types_tutorial_slam2d.cpp\n -rw-r--r--   0 root         (0) root         (0)     1613 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/types_tutorial_slam2d.h\n -rw-r--r--   0 root         (0) root         (0)     1871 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/vertex_point_xy.cpp\n -rw-r--r--   0 root         (0) root         (0)     2148 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/vertex_point_xy.h\n -rw-r--r--   0 root         (0) root         (0)     1879 2023-08-06 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./usr/share/doc/libg2o-dev/html/FlexLexer_8h__dep__incl.md5\n -rw-r--r--   0 root         (0) root         (0)    39947 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/html/FlexLexer_8h__dep__incl.png\n -rw-r--r--   0 root         (0) root         (0)      503 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/html/FlexLexer_8h__incl.map\n -rw-r--r--   0 root         (0) root         (0)       32 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/html/FlexLexer_8h__incl.md5\n"}, {"source1": "./usr/share/doc/libg2o-dev/g2o.pdf.gz", "source2": "./usr/share/doc/libg2o-dev/g2o.pdf.gz", "unified_diff": null, "details": [{"source1": "g2o.pdf", "source2": "g2o.pdf", "unified_diff": null, "details": [{"source1": "pdftotext {} -", "source2": "pdftotext {} -", "unified_diff": "@@ -1,12 +1,12 @@\n g2o: A general Framework for (Hyper) Graph Optimization\n Giorgio Grisetti, Rainer Ku\u0308mmerle, Hauke Strasdat, Kurt Konolige\n email: {grisetti,kuemmerl}@informatik.uni-freiburg.de\n strasdat@gmail.com konolige@willowgarage.com\n-April 3, 2026\n+March 2, 2025\n In this document we describe a C++ framework for performing the optimization of nonlinear least\n squares problems that 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. The naive\n implementation of standard methods, like Levenberg-Marquardt or Gauss-Newton can lead to acceptable\n"}]}]}]}]}]}]}