Visual navigation is of vital importance for autonomous mobile robots. Most existing practical perception-aware based visual navigation methods generally require prior-constructed precise metric maps, and learning-based methods rely on large training to improve their generality. To improve the reliability of visual navigation, in this paper, we propose a novel object-level topological visual navigation method. Firstly, a lightweight object-level topological semantic map is constructed to release the dependence on the precise metric map, where the semantic associations between objects are stored via graph memory and topological organization is performed. Then, we propose an object-based heuristic graph search method to select the global topological path with the optimal and shortest characteristics. Furthermore, to reduce the global cumulative error, a global path segmentation strategy is proposed to divide the global topological path on the basis of active visual perception and object guidance. Finally, to achieve adaptive smooth trajectory generation, a Bernstein polynomial-based smooth trajectory refinement method is proposed by transforming trajectory generation into a nonlinear planning problem, achieving smooth multi-segment continuous navigation. Experimental results demonstrate the feasibility and efficiency of our method on both simulation and real-world scenarios. The proposed method also obtains better navigation success rate (SR) and success weighted by inverse path length (SPL) than the state-of-the-art methods.

End-to-end visual navigation based on deep reinforcement learning (DRL) has recently attracted much attention. For most existing navigation methods, a robot moves along only fixed directions (e.g., up, down, left and right) on a grid. Obviously, they are not flexible and efficient, which worsens the navigation performance (i.e., the distance of movement and times of rotation). To address this problem, we propose a novel pose-guided end-to-end visual navigation framework, which is flexible and efficient. In the pose-guided navigation framework, a robot can move along arbitrary directions, which are determined by poses between adjacent objects. Further, to select a proper motion and finally form an optimal path, we propose a DRL based action-selected strategy, where a dynamic action select space on the basis of deep siamese actor-critic network is developed. Besides, to validate the proposed method, we propose a novel pose-guided dataset. Experimental results demonstrate that the proposed method outperforms the state of the arts in both flexibility and efficiency.