Redesign and analysis of globally asymptotically stable bearing only SLAM

Abstract

Abstract-The Simultaneous Localization And Mapping (SLAM) estimation problem is a nonlinear problem, due to the nature of the range and bearing measurements. In latter years it has been demonstrated that if the nonlinearities from the attitude are handled by a separate nonlinear observer, the SLAM dynamics can be represented as a linear time varying (LTV) system, by introducing these nonlinearities and nonlinear measurements as time varying vectors and matrices. This makes the SLAM estimation problem globally solvable with a Kalman filter, however, the noise structure is no longer trivial. In this paper, a new bearings only SLAM estimation algorithm is presented, including a novel design of the noise covariance matrices. Simulations of the SLAM estimator are presented, and show the performance of the state and uncertainty estimates, as well as the stability of the proposed estimator.

Estimating vector magnitude from its direction and derivative, with application to bearing-only SLAM filter problem

“…However, the setup is able to estimate the position of the vessel, and the distance to the landmarks by only having IMU, AHRS, bearing and optical flow measurements, and without dead reckoning. This also shows the duality between the range estimation problem, and speed estimation problem; implying that other globally stable observers can be used for velocity estimation fusing camera with IMU and AHRS data [7], [8], [11], making velocity measurements redundant. V. CONCLUSION We presented a novel vector magnitude observer, which uses unit vector measurement and derivative.…”

“…These are optimization based solutions often resulting in accurate estimates, however, they are computationally demanding, and guaranteed stability can often be difficult if not impossible to acquire. This has given some motivation to attack the SLAM filter problem with nonlinear observers (NLO), as they usually have complimentary characteristics to the mentioned methods: defined stability traits with defined region of attraction, low computational cost, although lacking optimality when they handle noisy measurements [7], [8]. Other NLO approaches for the SLAM problem are presented in [9]- [11], where [10] and [11] assume velocity measurement or biased velocity measurement, while in [9] the authors present a NLO for fusing measurements from the homography with IMU data.…”

Globally stable velocity estimation using normalized velocity measurement

“…It was also seen that the fixed gain observers were easier to tune, and could reuse their tuning on the experimental data set. The proposed setup can either be used for initializing a VO or VIO algorithms if a calibrated IMU/AHRS is available or it can provide globally stable velocity estimates for globally stable bearing-only navigation methods such as in Bjørne et al (2017), Le Bras et al (2017), andLourenc xo et al (2018), paving the way for a globally stable SLAM solution using a self-calibrating IMU and camera.…”

Constructive Observer Design for Visual Simultaneous Localisation and Mapping

“…For the VSLAM problem, where only bearing measurements are available, Lourenco et al [13,14] proposed an observer with a globally exponentially stable error system using depths of landmarks as separate components of the observer. Bjorne et al [4] use an attitude heading reference system (AHRS) to determine the orientation of the robot, and then solve the SLAM problem using a linear Kalman filter. A similar approach to VSLAM is undertaken by Le Bras et al [6].…”