Depth completion refers to inferring the dense depth map from a sparse depth map with or without corre-sponding color image. Numerous neural networks have been proposed to accomplish this task. However, insufficient uti-lization of heteromorphic data and the fact that predicted dense depth prefers a sparse depth enormously damage the performance of approaches. To reduce data preference and fully utilize two modalities, this paper proposes a novel network that predicts high- and low-frequency components of dense depth separately. Specifically, the framework consists of a Low-Frequency(LF) branch and a High-Frequency(HF) branch. In the LF branch, we recover the low-frequency depth component from sparse depth through an Adaptive Graph-Generate Graph Attention Network, which can be seen as a low-pass filter. In the HF branch, we model the high-frequency component, e.g. boundaries, as residuals to mitigate the impact of data preferences. Moreover, in this branch, we propose an Attention-based Self-Fusion mechanism to efficiently fuse multi-scale features extracted from the sparse depth and color image. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on the KITTI benchmark and ranks 1st in root mean squared error among other published approaches.

Feature extraction plays an important role in visual localization. Unreliable features on dynamic objects or repetitive regions will interfere with feature matching and challenge indoor localization greatly. To address the problem, we propose a novel network, RaP-Net, to simultaneously predict region-wise invariability and point-wise reliability, and then extract features by considering both of them. We also introduce a new dataset, named OpenLORIS-Location, to train the proposed network. The dataset contains 1553 images from 93 indoor locations. Various appearance changes between images of the same location are included and can help the model to learn the invariability in typical indoor scenes. Experimental results show that the proposed RaP-Net trained with OpenLORIS-Location dataset achieves excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization. The RaPNet code and dataset are available at https://github.com/ivipsourcecode/RaP-Net.

Loop closure detection (LCD) is a crucial technique for robots, which can correct accumulated localization errors after long time explorations. In this paper, we propose a robust LCD algorithm based on Bag of SuperPoints and graph verification. The system first extracts interest points and feature descriptors using the SuperPoint neural network. Then a visual vocabulary is trained in an incremental and self-supervised manner considering the relations between consecutive training images. Finally, a topological graph is constructed using matched feature points to verify candidate loop closures obtained by a Bag-of-Words (BoW) framework. Comparative experiments with state-of-the-art LCD algorithms on several typical datasets have been carried out. The results demonstrate that our proposed graph verification method can significantly improve the accuracy of image matching and the overall LCD approach outperforms existing methods.

The paper Automatic visual bag-of-words for online robot navigation and mapping by T. Nicosevici and R. Garcia (IEEE Trans. Robot., vol. 28, no. 4, pp. 886898, Aug. 2012) proposed an algorithm for robot navigation and mapping. In this paper, we correct some errors in their analysis.

To solve the problem that the final velocity curve will be unsmoothed if the PVT(Position Velocity Time) nodes velocities are determined improperly when using PVT control algorithm, based on analyzing the general principle of spline interpolation and PVT motion control, this paper presents a triple spline interpolation based PVT algorithm, which ensures the smoothness of the velocity curve. The velocity of each node is firstly calculated via spline interpolation method, and then the final trace control curve is achieved by using PVT method to these points. The proposed algorithm is applied to the kinematics control of a cockroach robot. The structure and forward/inverse kinematics model of the cockroach robot is expounded. Finally, the reliability of the proposed algorithm is verified by the contrast simulation and experiment of a single leg of the cockroach robot with one of the existing algorithms. The results show that the proposed approach can be readily used for motion controls.