Abstract:
The fusion of data from multiple sensors and the optimization of the 6-DoF pose add
significant challenges in Visual Inertial SLAM, impacting the localization and mapping
processes and necessitating human supervision for the robot. Ideally, a robot engaging in
Visual Inertial SLAM should be capable of navigating through an unknown environment,
avoiding obstacles, and comprehensively mapping the surroundings. The success of
SLAM algorithms hinges entirely on the input from on-board sensors. In the event
of noisy data output by these sensors, these algorithms fail, causing the robot to lose
its way, as the image features are not matched frame-by-frame, a crucial requirement
for uninterrupted SLAM operation. Once the tracking is lost, the mapping process is
disrupted, and even though the robot is unaware, it continues to move, posing a problem.
Various sensors have inherent limitations, such as IMU being susceptible to high drifts
and LIDAR requiring line of sight. Therefore, there is a need for a method to efficiently
fuse data from all sensors to produce highly accurate readings. Some SLAM algorithms
employ full graph optimization, but this is computationally expensive, requiring expensive
on-board computers for implementation. Hence, there must be an approach to
efficiently fuse sensor data that can be integrated into SLAM algorithms suitable for
resource-constrained platforms like Jetson Nano or Smart Phones.
Our research addresses the challenge of creating a more comprehensive and accurate
perception of the environment by introducing a new framework for vision-aided inertial
navigation. This framework fuses information from multiple sensors, including the
camera and IMU, in both tightly and loosely coupled fashion. We employ Semi Sparse
ORB-Assisted MSCKF (Multi-State Constraint Kalman Filter), Corner-based ISPKF
(Iterated Sigma Point Kalman Filter) State Estimator, and UKF (Unscented Kalman
Filter) to enhance accuracy and redundancy. Additionally, these methods handle nonlinearities
in the system dynamics and measurement equations simultaneously.
