A Dense Segmentation Network for Fine Semantic Mapping This paper proposes a fine semantic mapping method using dense segmentation network (DSNet) to obtain good performance of semantic mapping fusion, in which the semantic segmentation network (DSNet) is constructed based on the idea of DenseNetxe2x80x99s dense connection. First, the RGB image and the depth image are used to generate a dense indoor scene map via the stateoftheart dense SLAM (ElasticFusion). Then, semantic segmentation are precisely performed on the input RGB image via DSNet. Finally, the longterm correspondence between the landmarks and the indoor scene map is established using the continuous frames both in the visual odometer and loop detection, and the final fused semantic map is obtained by integrating semantic predictions of the RGBD video frames of multiple angles with the indoor scene map. Experiments were performed on the NYUv2 and CIFAR10 datasets and our laboratory environments. Results show shows that our method can reduce the error of dense map construction and obtain good semantic segmentation performance.

A DenseNet featurebased loop closure method for visual SLAM system * Loop closure is a crucial part in SLAM, especially for large and longterm scenes. Utilizing offtheshelf networksxe2x80x99 features in loop closure becomes a hot spot. However, what kind of network is more suitable in loop closure and how to use their features have not been wellstudied. In this paper, DenseNet is introduced in this field according to its own characters. The features of DenseNet preserve both semantic information and structure details and outweigh other popular networksxe2x80x99 features significantly. Based on this, a DenseNet featurebased framework, named DenseLoop, is proposed to address the loop closure problem. Weighted Vector of Locally Aggregated Descriptor (WVLAD) method is used to encode the local descriptors as the final global descriptor, which could resist geometry structure and viewpoint changes. Furthermore, 4 maxpooling by channel and localitysensitive hashing (LSH) are adopted to accelerate the search process. Extensive experiments are conducted on public datasets and the results demonstrate DenseLoop could achieve stateoftheart performance.