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https://github.com/ganlumomo/BKISemanticMapping

Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping
https://github.com/ganlumomo/BKISemanticMapping
semantic-mapping
Last synced: 2 months ago
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Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping
Host: GitHub
URL: https://github.com/ganlumomo/BKISemanticMapping
Owner: ganlumomo
Created: 2019-09-07T18:50:23.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2024-03-25T00:03:21.000Z (9 months ago)
Last Synced: 2024-08-01T00:38:28.035Z (5 months ago)
Topics: semantic-mapping
Language: C++
Size: 7.93 MB
Stars: 121
Watchers: 5
Forks: 21
Open Issues: 2
Metadata Files:
- Readme: README.md
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README

        # BKISemanticMapping

Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping





## Quantitative results on SemanticKITTI dataset sequence 00-10 for 19 semantic classes. SqueezeSegV2-kNN (Sq.-kNN)

| Sequence  | Method | Car | Bicycle | Motorcycle | Truck | Other Vehicle | Person | Bicyclist | Motorcyclist | Road | Parking | Sidewalk | Other Ground | Building | Fence | Vegetation | Trunk | Terrain | Pole | Traffic Sign | Average | 

| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

| 00 | Sq.-KNN | 92.1 | 18.3 | 55.0 | 76.5 | 62.9 | 34.2 | 52.0 | 61.4 | 94.7 | 71.0 | 87.9 | 1.2 | 89.8 | 54.6 | 82.2 | 53.1 | 79.3 | 38.6 | 51.5 | 60.9 |

| | S-CMS | 95.6 | 23.5 | 69.8 | 88.3 | 74.4 | 47.9 | 71.6 | 56.9 | **96.3** | 78.1 | 91.2 | 3.1 | 93.6 | 64.2 | 87.4 | 70.1 | 83.5 | 61.1 | 70.7 | 69.9 |

| | S-BKI | **96.9** | **26.5** | **75.8** | **93.5** | **80.1** | **61.5** | **77.5** | **71.0** | 96.2 | **79.2** | **91.5** | **6.6** | **94.6** | **66.5** | **88.9** | **73.4** | **84.5** | **65.8** | **76.2** | **75.0** |

| 01 | Sq.-KNN | 83.8 | n/a | n/a | n/a | 82.9 | n/a | n/a | 67.9 | 92.6 | n/a | n/a | 70.5 | 58.0 | 71.4 | 72.1 | 18.0 | 71.5 | 21.8 | 68.9 | 64.0 |

| | S-CMS | 89.8 | n/a | n/a | n/a | 91.0 | n/a | n/a | 70.3 | 93.4 | n/a | n/a | 74.2 | 64.4 | 73.8 | 75.1 | 26.3 | 74.7 | 31.9 | 78.7 | 70.3 |

| | S-BKI | **91.0** | n/a | n/a | n/a | **96.0** | n/a | n/a | **70.7** | **94.3** | n/a | n/a | **75.2** | **67.1** | **75.1** | **76.4** | **30.6** | **76.1** | **36.2** | **81.4** | **72.5** |

| 02 | Sq.-KNN | 90.9 | 14.5 | 50.8 | n/a | 56.4 | 38.6 | n/a | 59.9 | 93.9 | 68.1 | 84.9 | 50.9 | 79.1 | 66.1 | 82.5 | 48.9 | 68.3 | 25.7 | 35.9 | 59.7 |

| | S-CSM | 95.4 | 28.5 | 73.4 | n/a | 80.3 | 60.3 | n/a | 75.1 | **94.8** | 74.4 | **87.4** | 61.7 | 85.0 | 71.8 | 86.7 | 66.9 | 72.9 | 43.5 | 55.7 | 71.4 |

| | S-BKI | **95.8** | **31.1** | **76.4** | n/a | **83.3** | **62.5** | n/a | **79.5** | **94.8** | **75.0** | **87.4** | **63.6** | **85.6** | **72.1** | **87.1** | **68.8** | **73.4** | **45.9** | **60.4** | **73.1** |

| 03 | Sq.-KNN | 88.4 | 21.9 | n/a | 12.4 | 60.1 | 16.3 | n/a | n/a | 92.8 | 57.9 | 83.2 | n/a | 77.4 | 70.1 | 79.3 | 41.6 | 62.3 | 35.9 | 47.3 | 56.5 |

| | S-CSM | 92.4 | 29.7 | n/a | 23.1 | 65.4 | 17.6 | n/a | n/a | **94.3** | 69.4 | 86.9 | n/a | 80.4 | 73.8 | 83.2 | 52.3 | 66.9 | 53.5 | 62.0 | 63.0 |

| | S-BKI | **94.5** | **42.4** | n/a | **48.8** | **73.6** | **23.8** | n/a | n/a | **94.3** | **73.2** | **87.2** | n/a | **82.1** | **74.7** | **84.1** | **55.7** | **67.4** | **57.3** | **66.7** | **68.0** |

| 04 | Sq.-KNN | 84.9 | n/a | n/a | n/a | 68.1 | 20.8 | n/a | n/a | 95.8 | 26.1 | 68.4 | 61.5 | 49.3 | 76.4 | 82.6 | 14.0 | 67.6 | 36.0 | 44.6 | 56.9 |

| | S-CSM | **88.3** | n/a | n/a | n/a | 71.2 | 23.2 | n/a | n/a | **96.5** | 40.5 | **72.5** | 64.0 | 52.1 | 78.5 | 85.5 | 19.4 | 72.5 | 50.8 | 57.6 | 62.3 |

| | S-BKI | 87.7 | n/a | n/a | n/a | **82.5** | **37.3** | n/a | n/a | 96.2 | **55.7** | 72.3 | **68.3** | **56.9** | **80.3** | **87.1** | **24.4** | **72.7** | **55.5** | **67.0** | **67.5** |

| 05 | Sq.-KNN | 89.1 | 8.6 | 15.4 | 82.5 | 70.9 | 31.0 | 55.0 | n/a | 94.7 | 84.8 | 85.0 | 61.5 | 87.0 | 72.4 | 75.5 | 30.3 | 64.6 | 27.6 | 39.5 | 59.8 |

| | S-CSM | **93.4** | 15.4 | 28.9 | 86.4 | 78.4 | 39.8 | 69.4 | n/a | **96.4** | **90.1** | **88.5** | 70.0 | 90.9 | **77.7** | 81.2 | 46.8 | **69.5** | 47.6 | 57.4 | 68.2 |

| | S-BKI | 93.2 | **27.4** | **46.0** | **89.0** | **84.1** | **47.5** | **83.3** | n/a | 94.2 | 88.0 | 83.6 | **75.2** | **92.4** | 75.3 | **82.1** | **53.5** | **69.5** | **50.2** | **63.3** | **72.1** |

| 06 | Sq.KNN | 85.4 | 17.1 | 50.2 | 86.7 | 66.1 | 27.6 | 64.3 | n/a | 87.6 | 56.0 | 74.9 | 66.5 | 83.9 | 38.4 | 61.9 | 32.0 | 89.5 | 40.1 | 52.7 | 60.1 |

| | S-CSM | 91.8 | 22.7 | 62.5 | 89.8 | 75.4 | 43.3 | 92.1 | n/a | **91.1** | 68.2 | **80.4** | 70.5 | 89.4 | 49.3 | 69.7 | 50.1 | **92.2** | 60.0 | 77.9 | 70.9 |

| | S-BKI | **92.6** | **28.7** | **67.9** | **93.5** | **81.4** | **62.7** | **95.4** | n/a | 90.3 | **70.7** | 79.9 | **71.8** | **91.7** | **53.6** | **73.7** | **54.7** | 91.9 | **66.4** | **84.8** | **75.1** |

| 07 | Sq.-KNN | 92.4 | 21.3 | 64.0 | **83.6** | 69.8 | 53.2 | 63.6  | n/a | 93.9 | 75.9 | 89.3 | n/a | 90.9 | 59.7 | 76.5 | 45.9 | 82.8 | 40.2 | 54.0 | 68.1 |

| | S-CSM | **94.9** | 25.9 | 76.8 | 82.6 | 81.5 | 64.2 | 88.0 | n/a | **95.8** | **80.9** | **92.0** | n/a | 93.8 | **66.6** | 80.8 | 59.8 | **84.7** | **55.4** | 73.2 | 76.2 |

| | S-BKI | 93.8 | **29.2** | **80.2** | 82.7 | **87.8** | **70.1** | **92.7** | n/a | 93.9 | 77.0 | 87.7 | n/a | **94.1** | 63.4 | **81.4** | **84.1** | 84.5 | 53.2 | **77.6** | **77.2** |

| 08 | Sq.-KNN | 86.7 | 14.4 | 24.6 | 21.0 | 23.3 | 23.5 | 40.9 | n/a | 90.1 | 32.4 | 74.8 | **1.2** | 79.6 | 42.7 | 79.2 | 36.5 | 71.1 | 28.3 | 24.8 | 44.1 |

| | S-CSM | 90.5 | 23.0 | 34.9 | 26.8 | 29.1 | 32.4 | 49.4 | n/a | 92.6 | 38.7 | 79.0 | 1.1 | 84.6 | 51.6 | 83.3 | 48.3 | 72.9 | 44.1 | 31.6 | 50.8 |

| | S-BKI | **92.3** | **30.0** | **39.7** | **29.3** | **32.1** | **38.8** | **54.7** | n/a | **92.9** | **40.9** | **79.9** | 1.1 | **86.6** | **54.6** | **84.9** | **52.3** | **74.2** | **47.9** | **34.7** | **53.7** |

| 09 | Sq.-KNN | 89.2 | 5.3 | 48.0 | 79.8 | 61.3 | 37.3 | n/a | n/a | 91.0 | 59.0 | 79.9 | **38.9** | 80.9 | 62.9 | 77.0 | 32.3 | 61.7 | 31.8 | 52.6 | 58.2 |

| | S-CSM | 93.9 | 12.2 | 71.9 | 85.6 | 71.6 | 47.5 | n/a | n/a | **91.8** | 67.0 | 83.1 | 23.4 | 88.9 | 65.7 | 82.6 | 42.9 | 64.9 | 52.4 | 53.0 | 64.6 |

| | S-BKI | **96.0** | **22.8** | **80.2** | **90.5** | **79.7** | **60.7** | n/a | n/a | 91.7 | **70.0** | **83.8** | 30.7 | **90.8** | **69.1** | **84.0** | **46.3** | **66.0** | **59.1** | **58.2** | **69.4** 

| 10 | Sq.-KNN | 84.0 | 8.1 | 36.2 | 49.3 | 10.2 | 40.9 | n/a | n/a | 89.4 | 59.6 | 78.5 | 42.7 | 76.7 | 64.2 | 77.6 | 29.0 | 67.8 | 30.7 | 47.9 | 52.0 |

| | S-CSM | 91.0 | 14.6 | 51.8 | 67.7 | 16.6 | 52.8 | n/a | n/a | 92.1 | 69.7 | 83.7 | 51.3 | 81.7 | 70.0 | 82.2 | 43.3 | 72.4 | 51.7 | 64.1 | 62.1 |

| | S-BKI | **93.8** | **24.6** | **60.3** | **76.2** | **21.2** | **65.0** | n/a | n/a | **92.3** | **73.4** | **84.8** | **54.5** | **83.0** | **71.2** | **83.4** | **47.3** | **73.4** | **56.2** | **67.9** | **66.4** |

| Average | Sq.-KNN | 87.9 | 14.4 | 43.0 | 61.5 | 57.5 | 32.3 | 55.2 | 63.1 | 92.4 | 59.1 | 80.7 | 43.9 | 77.5 | 61.7 | 76.9 | 34.7 | 71.5 | 32.4 | 47.2 | 57.6 |

| | S-CSM | 92.5 | 21.7 | 58.7 | 68.8 | 66.8 | 42.9 | 74.1 | 67.4 | **94.1** | 67.7 | **84.5** | 46.6 | 82.3 | 67.5 | 81.6 | 47.8 | 75.2 | 50.2 | 62.0 | 65.9 |

| | S-BKI | **93.4** | **29.2** | **65.8** | **75.4** | **72.9** | **93.0** | **80.7** | **73.7** | 93.7 | **72.3** | 83.8 | **49.0** | **84.1** | **68.7** | **83.0** | **53.7** | **75.8** | **54.0** | **67.1** | **72.1** |

## Quantitative results on SemanticKITTI dataset sequence 00-10 for 19 semantic classes. Darknet53-kNN (Da-kNN)

| Sequence  | Method | Car | Bicycle | Motorcycle | Truck | Other Vehicle | Person | Bicyclist | Motorcyclist | Road | Parking | Sidewalk | Other Ground | Building | Fence | Vegetation | Trunk | Terrain | Pole | Traffic Sign | Average | 

| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

| 00 | Da-kNN | 0.960| 0.418 | 0.828 | 0.928 | 0.902 | 0.703 | 0.726 | 0.573 | 0.971 | 0.874 | 0.939 | 0.311 | 0.967 | 0.817 | 0.930 | 0.792 | 0.906 | 0.733 | 0.852 | 0.796 |

| | S-CSM | 0.975 | 0.476 | 0.897 | 0.957 | 0.937 | 0.787 | 0.829 | 0.640 | 0.974 | 0.891 | 0.949 | 0.419 | 0.977 | 0.852 | 0.948 | 0.864 | 0.924 | 0.832 | 0.897 | 0.843 |

| | S-BKI | 0.980 | 0.509 | 0.917 | 0.972 | 0.955 | 0.830 | 0.865 | 0.790 | 0.971 | 0.888 | 0.943 | 0.426 | 0.980 | 0.855 | 0.953 | 0.878 | 0.928 | 0.847 | 0.900 | 0.862 |

| 01 | Da-kNN | 0.860 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.582 | 0.956 | 0.000 | 0.000 | 0.832 | 0.924 | 0.793 | 0.873 | 0.557 | 0.863 | 0.606 | 0.887 | 0.460 |

| | S-CSM | 0.870 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.534 | 0.962 | 0.000 | 0.000 | 0.843 | 0.929 | 0.806 | 0.880 | 0.600 | 0.874 | 0.694 | 0.922 | 0.469 |

| | S-BKI | 0.889 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.586 | 0.963 | 0.000 | 0.000 | 0.849 | 0.959 | 0.825 | 0.895 | 0.691 | 0.883 | 0.747 | 0.942 | 0.486 |

| 02 | Da-kNN | 0.952 | 0.292 | 0.819 | 0.000 | 0.877 | 0.698 | 0.023 | 0.754 | 0.968 | 0.887 | 0.924 | 0.755 | 0.927 | 0.852 | 0.934 | 0.753 | 0.880 | 0.637 | 0.738 | 0.720 |

| | S-CSM | 0.961 | 0.339 | 0.859 | 0.000 | 0.908 | 0.798 | 0.020 | 0.754 | 0.966 | 0.894 | 0.924 | 0.780 | 0.942 | 0.861 | 0.943 | 0.824 | 0.895 | 0.723 | 0.807 | 0.747 |

| | S-BKI | 0.970 | 0.398 | 0.896 | 0.000 | 0.936 | 0.855 | 0.015 | 0.858 | 0.963 | 0.897 | 0.922 | 0.806 | 0.952 | 0.863 | 0.948 | 0.845 | 0.902 | 0.748 | 0.839 | 0.769 |

| 03 | Da-kNN | 0.943 | 0.393 | 0.000 | 0.712 | 0.889 | 0.473 | 0.000 | 0.000 | 0.971 | 0.828 | 0.935 | 0.000 | 0.930 | 0.880 | 0.951 | 0.645 | 0.922 | 0.722 | 0.782 | 0.630 |

| | S-CSM | 0.957 | 0.525 | 0.000 | 0.685 | 0.903 | 0.496 | 0.000 | 0.000 | 0.973 | 0.852 | 0.942 | 0.000 | 0.947 | 0.895 | 0.961 | 0.709 | 0.935 | 0.802 | 0.827 | 0.653 |

| | S-BKI | 0.970 | 0.684 | 0.000 | 0.705 | 0.939 | 0.618 | 0.000 | 0.000 | 0.971 | 0.871 | 0.937 | 0.000 | 0.955 | 0.894 | 0.964 | 0.724 | 0.938 | 0.820 | 0.837 | 0.675 |

| 04 | Da-kNN | 0.908 | 0.000 | 0.000 | 0.000 | 0.915 | 0.433 | 0.000 | 0.000 | 0.985 | 0.728 | 0.883 | 0.807 | 0.922 | 0.928 | 0.936 | 0.312 | 0.888 | 0.719 | 0.742 | 0.584 |

| | S-CSM | 0.925 | 0.000 | 0.000 | 0.000 | 0.919 | 0.467 | 0.000 | 0.000 | 0.987 | 0.763 | 0.900 | 0.823 | 0.935 | 0.935 | 0.946 | 0.370 | 0.905 | 0.795 | 0.810 | 0.604 |

| | S-BKI | 0.947 | 0.000 | 0.000 | 0.000 | 0.955 | 0.581 | 0.000 | 0.000 | 0.988 | 0.804 | 0.906 | 0.845 | 0.949 | 0.946 | 0.951 | 0.400 | 0.915 | 0.815 | 0.830 | 0.623 |

| 05 | Da-kNN | 0.910 | 0.465 | 0.612 | 0.925 | 0.535 | 0.655 | 0.700 | 0.000 | 0.973 | 0.937 | 0.921 | 0.848 | 0.947 | 0.874 | 0.892 | 0.649 | 0.791 | 0.698 | 0.788 | 0.743 |

| | S-CSM | 0.923 | 0.517 | 0.695 | 0.941 | 0.539 | 0.713 | 0.822 | 0.000 | 0.967 | 0.938 | 0.925 | 0.865 | 0.958 | 0.891 | 0.910 | 0.761 | 0.821 | 0.789 | 0.843 | 0.780 |

| | S-BKI | 0.938 | 0.591 | 0.774 | 0.957 | 0.563 | 0.820 | 0.913 | 0.000 | 0.974 | 0.944 | 0.928 | 0.876 | 0.972 | 0.900 | 0.926 | 0.798 | 0.842 | 0.807 | 0.869 | 0.810 |

| 06 | Da-kNN | 0.935 | 0.460 | 0.721 | 0.728 | 0.650 | 0.708 | 0.791 | 0.000 | 0.948 | 0.831 | 0.893 | 0.857 | 0.956 | 0.780 | 0.844 | 0.563 | 0.944 | 0.759 | 0.837 | 0.748 |

| | S-CSM | 0.955 | 0.547 | 0.810 | 0.742 | 0.675 | 0.838 | 0.923 | 0.000 | 0.963 | 0.851 | 0.922 | 0.884 | 0.971 | 0.833 | 0.876 | 0.711 | 0.959 | 0.880 | 0.931 | 0.804 |

| | S-BKI | 0.970 | 0.638 | 0.859 | 0.755 | 0.691 | 0.922 | 0.952 | 0.000 | 0.964 | 0.863 | 0.928 | 0.899 | 0.979 | 0.862 | 0.892 | 0.755 | 0.961 | 0.907 | 0.947 | 0.829 |

| 07 | Da-kNN | 0.961 | 0.446 | 0.876 | 0.911 | 0.939 | 0.768 | 0.798 | 0.000 | 0.970 | 0.888 | 0.946 | 0.000 | 0.971 | 0.816 | 0.894 | 0.769 | 0.910 | 0.750 | 0.869 | 0.762 |

| | S-SCM | 0.973 | 0.497 | 0.920 | 0.896 | 0.964 | 0.839 | 0.916 | 0.000 | 0.972 | 0.901 | 0.952 | 0.000 | 0.979 | 0.840 | 0.913 | 0.830 | 0.924 | 0.815 | 0.913 | 0.792 |

| | S-BKI | 0.979 | 0.523 | 0.936 | 0.905 | 0.980 | 0.880 | 0.945 | 0.000 | 0.971 | 0.899 | 0.948 | 0.000 | 0.982 | 0.844 | 0.921 | 0.845 | 0.929 | 0.823 | 0.921 | 0.802 |

| 08 | Da-kNN | 0.910 | 0.250 | 0.471 | 0.407 | 0.255 | 0.452 | 0.629 | 0.000 | 0.938 | 0.465 | 0.819 | 0.002 | 0.858 | 0.542 | 0.842 | 0.529 | 0.727 | 0.532 | 0.400 | 0.528 |

| | S-CSM | 0.926 | 0.325 | 0.549 | 0.434 | 0.262 | 0.513 | 0.692 | 0.000 | 0.946 | 0.492 | 0.840 | 0.001 | 0.879 | 0.584 | 0.858 | 0.599 | 0.733 | 0.617 | 0.430 | 0.562 |

| | S-BKI | 0.935 | 0.335 | 0.573 | 0.445 | 0.272 | 0.529 | 0.721 | 0.000 | 0.944 | 0.496 | 0.840 | 0.000 | 0.887 | 0.596 | 0.869 | 0.625 | 0.753 | 0.636 | 0.451 | 0.574 |

| 09 | Da-kNN | 0.909 | 0.349 | 0.774 | 0.851 | 0.376 | 0.582 | 0.000 | 0.000 | 0.963 | 0.859 | 0.913 | 0.755 | 0.941 | 0.864 | 0.919 | 0.576 | 0.853 | 0.723 | 0.823 | 0.686 |

| | S-CSM | 0.917 | 0.410 | 0.837 | 0.881 | 0.383 | 0.658 | 0.000 | 0.000 | 0.961 | 0.877 | 0.923 | 0.785 | 0.956 | 0.879 | 0.934 | 0.659 | 0.865 | 0.821 | 0.907 | 0.719 |

| | S-BKI | 0.932 | 0.490 | 0.887 | 0.904 | 0.392 | 0.723 | 0.000 | 0.000 | 0.962 | 0.875 | 0.925 | 0.797 | 0.966 | 0.897 | 0.943 | 0.678 | 0.888 | 0.848 | 0.920 | 0.738 |

| 10 | Da-kNN | 0.951 | 0.438 | 0.721 | 0.935 | 0.662 | 0.761 | 0.000 | 0.000 | 0.969 | 0.892 | 0.930 | 0.636 | 0.940 | 0.875 | 0.917 | 0.643 | 0.865 | 0.698 | 0.760 | 0.715 |

| | S-CSM | 0.965 | 0.466 | 0.801 | 0.950 | 0.679 | 0.841 | 0.000 | 0.000 | 0.974 | 0.909 | 0.944 | 0.670 | 0.959 | 0.898 | 0.935 | 0.743 | 0.890 | 0.796 | 0.841 | 0.751 |

| | S-BKI | 0.975 | 0.503 | 0.844 | 0.964 | 0.716 | 0.893 | 0.000 | 0.000 | 0.972 | 0.911 | 0.943 | 0.664 | 0.964 | 0.900 | 0.940 | 0.768 | 0.894 | 0.802 | 0.867 | 0.764 |

## Getting Started

### Building with catkin

```bash

catkin_ws/src$ git clone https://github.com/ganlumomo/BKISemanticMapping

catkin_ws/src$ cd ..

catkin_ws$ catkin_make

catkin_ws$ source ~/catkin_ws/devel/setup.bash

```

### Building using Intel C++ compiler (optional for better speed performance)

```bash

catkin_ws$ source /opt/intel/compilers_and_libraries/linux/bin/compilervars.sh intel64

catkin_ws$ catkin_make -DCMAKE_C_COMPILER=icc -DCMAKE_CXX_COMPILER=icpc

catkin_ws$ source ~/catkin_ws/devel/setup.bash

```

### Running the Demo

```bash

$ roslaunch semantic_bki toy_example_node.launch

```

## Semantic Mapping using KITTI dataset



### Download Data

Please download [data_kitti_15](https://drive.google.com/file/d/1dIHRrsA7rZSRJ6M9Uz_75ZxcHHY96Gmb/view?usp=sharing) and uncompress it into the data folder.

### Running

```bash

$ roslaunch semantic_bki kitti_node.launch

```

You will see semantic map in RViz. It also projects 3D grid onto 2D image for evaluation, stored at data/data_kitti_05/reproj_img.

### Evaluation

Evaluation code is provided in kitti_evaluation.ipynb. You may modify the directory names to run it.

## Semantic Mapping using SemanticKITTI dataset



### Download Data

Please download [semantickitti_04](https://drive.google.com/file/d/19Dv1jQqf-VGKS2qvbygFlUzQoSvu17E5/view?usp=sharing) and uncompress it into the data folder.

### Running

```bash

$ roslaunch semantic_bki semantickitti_node.launch

```

You will see semantic map in RViz. It also query each ground truth point for evaluation, stored at data/semantickitti_04/evaluations.

### Evaluation

Evaluation code is provided in semantickitti_evaluation.ipynb. You may modify the directory names to run it, or follow the guideline in [semantic-kitti-api](https://github.com/PRBonn/semantic-kitti-api) for evaluation.

## Relevant Publications

If you found this code useful, please cite the following:

Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping ([PDF](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8954837))

```

@ARTICLE{gan2019bayesian,

author={L. {Gan} and R. {Zhang} and J. W. {Grizzle} and R. M. {Eustice} and M. {Ghaffari}},

journal={IEEE Robotics and Automation Letters},

title={Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping},

year={2020},

volume={5},

number={2},

pages={790-797},

keywords={Mapping;semantic scene understanding;range sensing;RGB-D perception},

doi={10.1109/LRA.2020.2965390},

ISSN={2377-3774},

month={April},}

```

Learning-Aided 3-D Occupancy Mapping with Bayesian Generalized Kernel Inference ([PDF](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8713569))

```

@article{Doherty2019,

  doi = {10.1109/tro.2019.2912487},

  url = {https://doi.org/10.1109/tro.2019.2912487},

  year = {2019},

  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},

  pages = {1--14},

  author = {Kevin Doherty and Tixiao Shan and Jinkun Wang and Brendan Englot},

  title = {Learning-Aided 3-D Occupancy Mapping With Bayesian Generalized Kernel Inference},

  journal = {{IEEE} Transactions on Robotics}

}

```