Image-goal navigation in unknown environments with sensor error is of considerable difficulty for autonomous robots. In this paper, we propose a drift-resisting topo-metric graph to map the environment and localize the robot using only relative poses. The error-sharing mechanism under this representation effectively reduces the impact of accumulated drifts commonly encountered in navigation tasks. A Reinforcement Learning based policy was proposed for sub-goal selection on this topo-metric graph, which improves navigation efficiency by handling task-driven features taking both image correlation and topological layout into account. We adopt a modular system design with this map representation and graph policy, leaving the low-level motion planning problems to classical controllers for better stability and generalizability. Experimental results demonstrate that our method can achieve robust navigation performance in a variety of unknown environments and even 50% higher success rate over existing methods in complex environments with odometry drift.

Global trajectory planning is crucial for long-range formation navigation tasks of multi-robot systems in efficiency improvement and energy saving, whose main challenges are the joint space constraints of the whole team and the long-range deployment. To overcome the above difficulties, we reformulate the original problem into an affine formation planning problem in parameter space. Further, we propose a front-end & back-end framework for global trajectory planning of Multi-Robot Systems (MRS) with affinely deformable formation. For the front-end, an RL-steering affine formation RRT* method is designed to search a global formation-level trajectory in affine parameter space, combining the efficient BVP-solving capability of RL and the global guidance and generalizing ability of RRT*. For the back-end, we propose a formationlevel affine parameter trajectory optimization method to refine the front-end trajectory, and further transform it into peragent trajectories for execution. Extensive benchmarks and ablation experiments in simulation show the effectiveness of our framework for the global trajectory generation of a multiUAV system with affinely deformable formation. The appendix can be seen here3.