Showing papers by "Jonghyuk Kim published in 2005"

6DoF SLAM aided GNSS/INS Navigation in GNSS Denied and Unknown Environments

Abstract: This paper presents the results of augmenting 6DoF Simultaneous Localisation and Mapping (SLAM) with GNSS/INS navigation system. SLAM algorithm is a feature based terrain aided navigation system that has the capability for online map building, and simultaneously utilising the generated map to constrain the errors in the on-board Inertial Navigation System (INS). In this paper, indirect SLAM is developed based on error analysis and then is integrated to GNSS/INS fusion filter. If GNSS information is available, the system performs feature- based mapping using the GNSS/INS solution. If GNSS is not available, the previously and/or newly generated map is now used to estimate the INS errors. Simulation results will be presented which shows that the system can provide reliable and accurate navigation solutions in GNSS denied environments for an extended period of time.

Recasting SLAM - Towards Improving Efficiency and Platform Independency

Abstract: This paper provides an alternative solution to solving SLAM’s computational complexity in Inertial Navigation System (INS) application, not from the perspective of map management techniques, but by focusing on the filter’s structure and model, and recasting the SLAM algorithm into what is known as an “indirect” implementation. In doing so we provide a navigation structure which is computationally efficient in even for highly non-linear, highly dynamic systems.

Towards Robust Airborne SLAM in Unknown Wind Environments

Abstract: This paper presents a robust multi-loop air- borne SLAM structure which augments wind information into the state of 6DoF Simultane- ous Localisation and Mapping (SLAM). The relative air velocity observation from an air data system can be used to estimate the error of the vehicle state. However due to a priori unknown wind information, it cannot directly be used for that purpose. This can be tack- led by augmenting this information into SLAM and estimating it simultaneously with the ve- hicle state. This can significantly increase the consistency of airborne SLAM at the time of loop closure. The air velocity based SLAM loop limits the error growth of the velocity and at- titude effectively and the feature based SLAM loop bounds the position error growth. Simu- lation results show that wind information can be estimated consistently and the robustness of airborne SLAM improves significantly. The current obstacle in implementing a robust air- borne SLAM is the inconsistency problem related with the closing-the-loop after long flight. The limited field of view of the airborne sensor and the sparse nature of the feature observations can inevitably increase the vehicle's uncertainties before the loop closure. The position error growth rate of the unaided INS is typically cubic func- tion of the flight time. This means there can be large uncertainties in the vehicle states when it tries to close the loop. However the large uncertainties can easily de- stroy the linearisation assumption in the SLAM filter, and any over-confident attitude correction can lead to a failure in SLAM. This is due to the fact that even a small attitude error will misinterpret the gravitational acceleration, thus resulting in a rapid divergence in the position and velocity. This, in turn, will easily invalidate and reject successive observations afterward. This problem can be tackled by using a more high- quality IMU, non-linear filtering, or adding additional in- formation to stabilise the attitude. If a high-quality IMU is an available option it should be selected in such a way that the INS error remains within a reasonable bound before closing the loop. The INS error growth, how- ever, depends on the availability of the features which is difficult to predict in advance. Additionally, many high- quality inertial sensors may not be a suitable option for most robotic platforms which are payload limited. The nonlinear filter, such as particle filter, can reduced the chance of divergence during the closing the loop. It was observed that it can improve the performance compared to the standard Kalman filter, but it still showed incon- sistency during the closing the loop. It also suffers the dimensionality problem. The effectiveness of this ap- proach needs further investigations in airborne SLAM applications. The stability of the attitude estimate is of importance for the reliable closing the loop. Hence if there exists any constant stream of information to limit the attitude uncertainty, it can improve the robustness of airborne SLAM. In many airborne applications, the air veloc-