VINS-MKF：A Tightly-Coupled Multi-Keyframe Visual-Inertial Odometry for Accurate and Robust State Estimation

Object-Based Reliable Visual Navigation for Mobile Robot

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.

A Hybrid Sliding Window Optimizer for Tightly-Coupled Vision-Aided Inertial Navigation System

The fusion of visual and inertial measurements for motion tracking has become prevalent in the robotic community, due to its complementary sensing characteristics, low cost, and small space requirements. This fusion task is known as the vision-aided inertial navigation system problem. We present a novel hybrid sliding window optimizer to achieve information fusion for a tightly-coupled vision-aided inertial navigation system. It possesses the advantages of both the conditioning-based method and the prior-based method. A novel distributed marginalization method was also designed based on the multi-state constraints method with significant efficiency improvement over the traditional method. The performance of the proposed algorithm was evaluated with the publicly available EuRoC datasets and showed competitive results compared with existing algorithms.