Software Releases

  • PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency
  • HortiMapping: Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots
  • PS_res-excite: Robust Double-Encoder Network for RGB-D Panoptic Segmentation
  • HAPT: Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain
  • SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations
  • Contrastive Association: Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans
  • KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way
  • 4DMOS: Moving Object Segmentation Using Sparse 4D Convolutions.
  • DCPCR: Deep Compressed Point Cloud Registration in Large-Scale Outdoor Environments.
  • 3DUIS: Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles.
  • SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination.
  • point-cloud-prediction: Self-supervised Prediction of Future 3D Point Clouds.
  • Retriever: Point Cloud Retrieval in Compressed 3D Maps.
  • DEPOCO: Deep Compression for Dense Point Cloud Maps.
  • LiDAR-MOS: Moving Object Segmentation in 3D LiDAR Data.
  • range_mcl: Range Image-based LiDAR Localization.
  • OverlapLocalization: Overlap-based LiDAR Monte Carlo Localization.
  • Matching_Sym_LSM: Symmetric least squares matching (Matlab).
  • OverlapNet: Loop Closing for LiDAR-based SLAM.
  • SuMa++: An efficient LiDAR-based semantic SLAM.
  • rangenet_lib: A c++ inferring example for RangeNet++.
  • Bonnetal: Easy-to-use Deep-learning Training and Deployment Pipeline
  • ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals
  • Bonnet: Tensorflow Convolutional Semantic Segmentation Pipeline
  • Fast Change Detection
  • MPR: General Framework for Flexible Multi-Cue Photometric Point Cloud Registration
  • Visual place recognition: Extended Version of Visual Place Recognition using Hashing
  • Online place recognition: a library for graph-based visual place recognition in changing environments
  • Depth Clustering: fast and robust Velodyne laser scan segmentation library
  • Software releases from the ROVINA Project
  • BACS: bundle adjustment for camera systems with omnidirectional cameras (Matlab, Python)
  • FastFPS: Fast marching for robust surface segmentation (Matlab)
  • Förstner operator: an interest point detector (Matlab)
  • SFOP: scale-invariant keypoint detector (Matlab, C++)
  • Completeness of Detectors: a measurement scheme for the completeness of a set of image feature detectors (Matlab)
  • IVM: Import Vector Machine classifier (Matlab, C++)
  • SUGR: geometric reasoning with uncertain elements of projective geometry (Java, Perl)
  • a general adjustment procedure for constrained observations with singular covariance matrices, e.g. projective geometry entities (Matlab)
  • an annotation tool for images implemented in Matlab, used for the eTRIMS Image Database
  • Depth Streaming using H.264 (C++, shell scripts)
  • C Implementation of the Hungarian Method: libhungarian-v0.1.3.tgz (21.09.2015)
    C-implementation of the Hungarian Method: finding the optimal assignment (assigning a set of jobs to a set of machines) in O(n^3), where n=max{#jobs, #machines}. The implementation is a sligntly enhanced version of the implementation provided by the
    Stanford GraphBase. See also: Stanford GraphBase, Hungarian Method by Brian Gerkey.
  • HOG-Man – Hierarchical Optimization for Pose Graphs on Manifolds: HOG-Man is our new back-end for graph-based SLAM which is also designed for online applications.
  • TORO – Tree-based netwORk Optimizer: TORO is a solution to compute low error configurations
    of constraint networks used in SLAM.
  • CARMEN: Download the latest release of CARMEN.
    Binary as well as source code/API documentation of CARMEN.
  • GMapping: Get GMapping, our SLAM approach using a Rao-Blackwellized particle filter (hosted at