The capability of autonomous exploration in complex, unknown environments is important in many robotic applications. While recent research on autonomous exploration have achieved much progress, there are still limitations, e.g., existing methods relying on greedy heuristics or optimal path planning are often hindered by repetitive paths and high computational demands. To address such limitations, we propose a novel exploration framework that utilizes the global topology information of observed environment to improve exploration efficiency while reducing computational overhead. Specifically, global information is utilized based on a skeletal topological graph representation of the environment geometry. We first propose an incremental skeleton extraction method based on wavefront propagation, based on which we then design an approach to generate a lightweight topological graph that can effectively capture the environment's structural characteristics. Building upon this, we introduce a finite state machine that leverages the topological structure to efficiently plan coverage paths, which can substantially mitigate the back-and-forth maneuvers (BFMs) problem. Experimental results demonstrate the superiority of our method in comparison with state-of-theart methods. The source code will be made publicly available at: https://github.com/Haochen-Niu/STGPlanner.

Semantic mapping based on information of objects has become a crucial component for the surrounding comprehension and the more robust navigation. In this paper, we propose a system for simultaneous localization and mapping (SLAM) that combines multiple objects tracking and factor graph optimization with semantically meaningful landmarks to achieve accurate monocular semantic mapping. Firstly, the process of object recognition uses a vanishing point sampling-based approach to efficiently infer the class and position of object landmarks from 2D bounding box object detection. Secondly, The semantic frontend utilizes local matching-based data association to track raw cuboid proposals. It can provide semantic constraints to reduce cuboid scale drift and improve its position estimation. Finally, we present a multi-view factor graph optimization which can use motion modal of the camera to optimize stable cuboids. The semantic mapping experiments on our own built virtual scene show better accuracy and robustness over existing approaches. We evaluate the effectiveness of our approach on public KITTI datasets and a real scene.

This paper presents a direct visual odometry method with adaptive stereo coupling factor and relocalization. The adaptive stereo coupling factor is calculated by considering the number of co-visibility frames, which makes stereo direct odometry more robust. The map is built up with keyframes, connecting relationship of keyframes, poses of keyframes and descriptors. The map will be saved automatically when the proposed system shuts down, and will be loaded automatically when the proposed system starts up. Our proposed system always builds a new map until a loop closure is found between new map and loaded map. When loop closure in a loaded map is found, the proposed system will locate camera in the loaded map. Moreover, our proposed system detects loop closure with feature-based bag-of-words (BoW) method on a low resolution level, which makes the loop closure detection achieve realtime. Evaluation results on the KITTI dataset show that, with the aid of the adaptive stereo coupling factor, the stereo visual odometry becomes more accurate and robust. And evaluation results in real world show that the proposed system can successfully detect loop closure and relocate in realtime.