Parth Agrawal
I am a Software Engineer at Google XR working on 3D vision based in Mountain View, CA. I'm interested in the intersection of computer vision and machine learning, with a focus on creating perception systems that can learn continuously and accurately interpret 3D geometry akin to human perception. I received my Bachelor's degree in Computer Science and Engineering from UCLA, where I worked on 3D vision and adversarial perturbations at the UCLA Vision Lab under Dr. Stefano Soatto and Dr. Alex Wong. My personal website can be found at https://agrawalparth10.github.io/.
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In this paper, we introduce a novel estimator for vision-aided inertial navigation systems (VINS), the Preconditioned Cholesky-based Square Root Information Filter (PC-SRIF). When working with linear systems, employing Cholesky decomposition offers superior efficiency but can compromise numerical stability. Due to this, existing VINS literature on (Square Root) Information Filters often opts for QR decomposition on platforms where single precision is preferred, avoiding the numerical challenges associated with Cholesky decomposition. While these issues are often attributed to the ill-conditioned information matrix in VINS, our analysis reveals that this is not an inherent property of VINS but rather a consequence of specific parametrizations. We identify several factors that contribute to an ill-conditioned information matrix and propose a preconditioning technique to mitigate these conditioning issues. Building on this analysis, we present PC-SRIF, which exhibits remarkable stability in performing Cholesky decomposition using single precision when solving linear systems in VINS. Consequently, PC-SRIF achieves superior theoretical efficiency compared to alternative estimators. To validate the efficiency advantages and numerical stability of PC-SRIF based VINS, we have conducted carefully controlled experiments, which provide empirical evidence in support of our theoretical findings. Empirically, in our VINS, PC-SRIF achieves more than 2x better efficiency than SRIF.
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