Jagatpreet Nir PhD Dissertation Defense

Announcing:
PhD Dissertation Defense

Name:
Jagatpreet Nir

Title:
Low Contrast Visual Sensing and Inertial-Aided Navigation in GPS-Denied  Environments

Date:
7/18/2024

Time:
11:00:00 AM

Committee Members:
Prof. Hanumant Singh (Advisor)
Prof. Martin Ludvigsen
Prof. Michael Everett
Prof. Pau Closas

Abstract:
Field robots perform complex tasks, necessitating high autonomy and reliable navigation capabilities. Integrating complementary sensors at the hardware level is crucial to maintaining navigation estimates even during sensor failure. This work is motivated by the need for robust and accurate navigation systems for robotic field applications, particularly in diverse and challenging environments. The development of such systems involves balancing design requirements with constraints such as size, weight, power, computational capacity, and cost. Underwater navigation exemplifies navigation in Visually Degraded Environments (VDEs), where Autonomous Underwater Vehicles (AUVs) and Remote Operated Vehicles ( ROVs) navigate in challenging conditions. This thesis focuses on exploring methods to enhance the robustness of visual-inertial odometry systems in VDEs.

The current state-of-the-art Visual Inertial Odometery (VIO) techniques provide high-accuracy navigation estimates in texture-rich scenes. However, robots operating in harsh and unpredictable environments, such as underwater, often encounter VDEs due to low texture, uneven illumination, or backscatter. During prolonged visual degradation, the Inertial Measurement Units (IMUs) become the primary sensor as visual measurements are unreliable. In this reserach, we address the problem of designing an underwater VIO navigation system and algorithmic pipelines to ensure reliable navigation estimates during several seconds of visual degradation, emphasizing the importance of selecting better Micro Electro Mechanical Systems (MEMS) IMUs for dependable performance within a cost budget.

A robust VIO system designed for underwater settings is introduced. Our contributions include a general system design approach for underwater VIO, an algorithmic formulation for fusing deep learning-based Visual Odometry (VO) with IMUs data. The underwater datasets depict visual degradation in real-world settings with a time-synchronized 8-bit grayscale camera and IMU. Our hybrid VIO pipeline integrates IMU measurements with VO estimates from a deep-learning VO engine, combining deep learning with classical sensor fusion techniques to achieve accurate metric and gravity-aligned trajectory estimates even in visually degraded conditions. The proposed system outperforms traditional VIO methods, demonstrating robustness with consistent trajectory estimates and minimal drift during complete visual outages. The extensible design allows for the incorporation of new sensors, addressing various underwater navigation challenges.

To conclude, this thesis focuses on environments where exteroceptive sensing, like cameras, is compromised for extended periods, relying on proprioceptive sensors such as IMUs to navigate. The aim is to quantify navigation accuracy in harsh environments and improve system design at both hardware and software levels. Specifically, underwater visual-inertial navigation for small vehicles is used to demonstrate the principles and algorithms developed. The outlined methodology showcases sensor selection, sensor-fusion algorithms, and individual improvements to build enhanced visual-inertial systems for VDEs and the applicability of the proposed approach from controlled settings to field tests.