Bearing-only Visual SLAM for Small Unmanned Aerial Vehicles in GPS-denied Environments

This paper presents a hierarchical simultaneous localization and mapping（SLAM） system for a small unmanned aerial vehicle（UAV） using the output of an inertial measurement unit（IMU） and the bearing-only observations from an onboard monocular camera.A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match.This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter（EKF） for attitude and velocity estimation.Then,another EKF is employed to estimate the position of the vehicle and the locations of the features in the map.Both simulations and experiments are carried out to test the performance of the proposed system.The result of the comparison with the referential global positioning system/inertial navigation system（GPS/INS） navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.

Trajectory Optimization for Target Localization and Sensor Bias Calibration with Bearing-Only Information

The problem of trajectory optimization of an unmanned aerial vehicle(UAV)for static target localization with biased bearing measurements is considered.The angular bias in sensor measurements is modeled as an additive constant in the observation model and jointly estimated with the position of the target.The necessary conditions for system observability of this estimation problem is first derived analytically with geometrical interpretations provided.The trajectory of UAV is designed based on the Fisher Information Matrix(FIM)considering physical constraints to enhance the system observability.Simulation results with Monte-Carlo runs are presented to demonstrate the improvement in target localization with biased measurements by UAV trajectory optimization.

A Velocity-Based Rao-Blackwellized Particle Filter Approach to Monocular vSLAM

This paper presents a modified Rao-Blackwellized Particle Filter (RBPF) approach for the bearing-only monocular SLAM problem. While FastSLAM 2.0 is known to be one of the most computationally efficient SLAM approaches;it is not applicable to certain formulations of the SLAM problem in which some of the states are not explicitly expressed in the measurement equation. This constraint impacts the versatility of the FastSLAM 2.0 in dealing with partially ob-servable systems, especially in dynamic environments where inclusion of higher order but unobservable states such as velocity and acceleration in the filtering process is highly desirable. In this paper, the formulation of an enhanced RBPF-based SLAM with proper sampling and importance weights calculation for resampling distributions is presented. As an example, the new formulation uses the higher order states of the pose of a monocular camera to carry out SLAM for a mobile robot. The results of the experiments on the robot verify the improved performance of the higher order RBPF under low parallax angles conditions.