For visual Simultaneous Localization and Mapping (VSLAM), the camera positional estimation is deeply affected by environmental factors such as the surrounding feature situation and light condition. In this case, the accuracy and robustness of the positional estimation in visual odometry can be improved by the assistance of artificial landmarks. Based on this, a correction method based on AprilTag is proposed for ORB-SLAM3 to optimize the pose estimation of landmark VSLAM. Firstly, a point cloud map is built with Tag mapping embedded through the high accuracy 6 DOF positional transformation data provided by AprilTag. With this, the tracking module of ORB-SLAM3 is improved the positioning accuracy during pose estimating. In this way, the computational resources for estimating the camera pose can be reduced while guaranteeing the accuracy of pose estimation. Through subsequent validation experiments, the effectiveness of AprilTag-aided solution for ORB-SLAM3 is demonstrated for pose estimation optimization of landmark VSLAM.

AprilTag-Aided Pose Estimation Optimization of Landmark VSLAM

This paper studies the problem of Cooperative Localization (CL) for multi-robot systems, where a group of mobile robots jointly localize themselves by using measurements from onboard sensors and shared information from other robots. We propose a novel distributed invariant Kalman Filter (DInEKF) based on the Lie group theory, to solve the CL problem in a 3-D environment. Unlike the standard EKF which computes the Jacobians based on the linearization at the state estimate, DInEKF defines the robots' motion model on matrix Lie groups and offers the advantage of state estimate-independent Jacobians. This significantly improves the consistency of the estimator. Moreover, the proposed algorithm is fully distributed, relying solely on each robot's ego-motion measurements and information received from its one-hop communication neighbors. The effectiveness of the proposed algorithm is validated in both Monte-Carlo simulations and real-world experiments. The results show that the proposed DInEKF outperforms the standard distributed EKF in terms of both accuracy and consistency. 

pdf/distributed-invariant-kalman-filter-for-cooperative-2tvnkyj50f.pdf

Distributed Invariant Kalman Filter for Cooperative Localization using
  Matrix Lie Groups

2D SLAM is useful for mobile robots that are constrained to a 2D plane, for example in a warehouse, simplifying calculations in respect to the 3D case. The use of an IMU in such a context can enrich the estimation and make it more robust. In this paper we reformulate the IMU preintegration widely used in 3D problems for the 2D case, making use of Lie Theory. The Lie theory based formalization, first derived for a perfectly horizontal plane, allows us to easily extend it to problems where the plane is not orthogonal to the gravity vector. We implement the theory in a factor graph based estimation library, and carry out experiments to validate it on a mobile platform. Two experiments are carried out; on a horizontal and a sloped environment, and the sensor data is processed using our two 2D methods and a state-of-the-art 3D method.

IMU preintegration for 2D SLAM problems using Lie Groups

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IMU preintegration for 2D SLAM problems using Lie Groups