Despite impressive advances in simultaneous localization and mapping, dense robotic mapping remains challenging due to its inherent nature of being a high-dimensional inference problem. In this paper, we propose a dense semantic robotic mapping technique that exploits sparse Bayesian models, in particular, the relevance vector machine, for high-dimensional sequential inference. The technique is based on the principle of automatic relevance determination and produces sparse models that use a small subset of the original dense training set as the dominant basis. The resulting map posterior is continuous, and queries can be made efficiently at any resolution. Moreover, the technique has probabilistic outputs per semantic class through Bayesian inference. We evaluate the proposed relevance vector semantic map using publicly available benchmark datasets, NYU Depth V2 and KITTI; and the results show promising improvements over the state-of-the-art techniques.
Semantic mapping for outdoor scenes is important for robot applications such as autonomous driving. @cite_20 , a 3D semantic reconstruction approach for outdoor scenes using 3D CRFs is proposed. However, to achieve large-scale dense 3D maps, memory and computational efficiency can cause a bottleneck. @cite_26 , the memory-friendly hash-based 3D volumetric representation and a CRF are used for incremental dense semantic mapping of large scenes with a near real-time processing time. Semantic Octree @cite_15 constructs a higher-order CRF model over voxels in OctoMap @cite_33 to a multi-resolution 3D semantic map representation; higher-oder cliques are naturally defined as internal nodes in the hierarchical octree data structure. @cite_28 a similar CRF model together with 3D scrolling occupancy grids are proposed and the reported results are using KITTI dataset @cite_19 are promising.
@cite_26 @cite_33 @cite_28 @cite_19 @cite_15 @cite_20
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In this paper we propose a robust algorithm that generates an efficient and accurate dense 3D reconstruction with associated semantic labellings. Intelligent autonomous systems require accurate 3D reconstructions for applications such as navigation and localisation. Such systems also need to recognise their surroundings in order to identify and interact with objects of interest. Considerable emphasis has been given to generating a good reconstruction but less effort has gone into generating a 3D semantic model. The inputs to our algorithm are street level stereo image pairs acquired from a camera mounted on a moving vehicle. The depth-maps, generated from the stereo pairs across time, are fused into a global 3D volume online in order to accommodate arbitrary long image sequences. The street level images are automatically labelled using a Conditional Random Field (CRF) framework exploiting stereo images, and label estimates are aggregated to annotate the 3D volume. We evaluate our approach on the KITTI odometry dataset and have manually generated ground truth for object class segmentation. Our qualitative evaluation is performed on various sequences of the dataset and we also quantify our results on a representative subset. Semantic reconstruction of a scene is important for a variety of applications such as 3D modelling, object recognition and autonomous robotic navigation. However, most object labelling methods work in the image domain and fail to capture the information present in 3D space. In this work we propose a principled way to generate object labelling in 3D. Our method builds a triangulated meshed representation of the scene from multiple depth estimates. We then define a CRF over this mesh, which is able to capture the consistency of geometric properties of the objects present in the scene. In this framework, we are able to generate object hypotheses by combining information from multiple sources: geometric properties (from the 3D mesh), and appearance properties (from images). We demonstrate the robustness of our framework in both indoor and outdoor scenes. For indoor scenes we created an augmented version of the NYU indoor scene dataset (RGBD images) with object labelled meshes for training and evaluation. For outdoor scenes, we created ground truth object labellings for the KITTY odometry dataset (stereo image sequence). We observe a significant speed-up in the inference stage by performing labelling on the mesh, and additionally achieve higher accuracies. 3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks (NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency. We present SEGCloud, an end-to-end framework to obtain 3D point-level segmentation that combines the advantages of NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields (FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation. Then the FC-CRF enforces global consistency and provides fine-grained semantics on the points. We implement the latter as a differentiable Recurrent NN to allow joint optimization. We evaluate the framework on two indoor and two outdoor 3D datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance comparable or superior to the state-of-the-art on all datasets. 3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks(NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency. We present SEGCloud, an end-to-end framework to obtain 3D point-level segmentation that combines the advantages of NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields (FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation. Then the FC-CRF enforces global consistency and provides fine-grained semantics on the points. We implement the latter as a differentiable Recurrent NN to allow joint optimization. We evaluate the framework on two indoor and two outdoor 3D datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance comparable or superior to the state-of-the-art on all datasets.