{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.1VjjcduJ/b1/g2o_0~20230806-4.1_i386.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.1VjjcduJ/b2/g2o_0~20230806-4.1_i386.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,5 +1,5 @@\n \n 637517f4cf73980bbcafda2c5be954e3 124044 libdevel optional libg2o-dev_0~20230806-4.1_i386.deb\n- 1c1b9ed5b6ef50f69f7d3e1c58445e23 209025640 doc optional libg2o-doc_0~20230806-4.1_all.deb\n+ 8c7accc1674333ea6fc471fb5f995244 209023896 doc optional libg2o-doc_0~20230806-4.1_all.deb\n 9d92dbde16ec0443313f69fa347d6ff6 152365772 debug optional libg2o0t64-dbgsym_0~20230806-4.1_i386.deb\n 7e5aff82892e361f21678f6a9cc2394f 829696 libs optional libg2o0t64_0~20230806-4.1_i386.deb\n"}, {"source1": "libg2o-doc_0~20230806-4.1_all.deb", "source2": "libg2o-doc_0~20230806-4.1_all.deb", "unified_diff": null, "details": [{"source1": "file list", "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 162396 2024-02-28 09:35:01.000000 control.tar.xz\n--rw-r--r-- 0 0 0 208863052 2024-02-28 09:35:01.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 162492 2024-02-28 09:35:01.000000 control.tar.xz\n+-rw-r--r-- 0 0 0 208861212 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 <debian-science-maintainers@lists.alioth.debian.org>\n-Installed-Size: 313565\n+Installed-Size: 313564\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": "data.tar.xz", "source2": "data.tar.xz", "unified_diff": null, "details": [{"source1": "data.tar", "source2": "data.tar", "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 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/vertex_se2.cpp\n -rw-r--r-- 0 root (0) root (0) 2156 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/vertex_se2.h\n--rw-r--r-- 0 root (0) root (0) 606463 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/g2o.pdf.gz\n+-rw-r--r-- 0 root (0) root (0) 606197 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/g2o.pdf.gz\n drwxr-xr-x 0 root (0) root (0) 0 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"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-March 9, 2025\n+April 12, 2026\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"}]}]}]}]}]}]}