{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.9IKYGCjP/b1/g2o_0~20230806-4.1_i386.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.9IKYGCjP/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- 5c8c91a65366ac87016e040d96f2a572 209024652 doc optional libg2o-doc_0~20230806-4.1_all.deb\n+ 5a80538b341d9f102983fbb3c97794d1 209022312 doc optional libg2o-doc_0~20230806-4.1_all.deb\n 83740e270f33b05c481192f1bd45f680 147479348 debug optional libg2o0t64-dbgsym_0~20230806-4.1_i386.deb\n 6f50ee026f74135162e78806603b9fe1 840744 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 162496 2024-02-28 09:35:01.000000 control.tar.xz\n--rw-r--r-- 0 0 0 208861964 2024-02-28 09:35:01.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 208859624 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: 313564\n+Installed-Size: 313563\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 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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-May 18, 2024\n+June 22, 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. 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