Abstract
As edge devices equipped with cameras and inertial measurement units (IMUs) are emerging, it holds huge implications to endow these mobile devices with spatial computing capability. However, ultra-efficient visual-inertial estimation at the size, weight and power (SWAP)-constrained edge devices to provide accurate 3D motion tracking remains challenging. This is exacerbated by data transfer (between different processors and memory) that consumes significantly more energy than computing itself. To push the state of the art, this paper proposes the first-of-its-kind quantized visual-inertial odometry (QVIO) to offer energy-efficient 3D motion tracking. In particular, we first quantize raw visual measurements in an intuitive way with a given small number of bits and then perform an EKF update with these quantized measurements (termed zQVIO). To improve this ad-hoc quantizer (although it works well in practice), we systematically quantize each measurement residual into a single bit and perform maximum- a-posterior (MAP) estimation. measurements. Thanks to these quantizers, the proposed QVIO estimators significantly reduce the data transfer and thus improve energy efficiency. As shown in our extensive experiments, the proposed residual-quantized VIO (rQVIO) achieves remarkably competing performance even when using an average of only 3.7 bits per measurement, equivalent to a data reduction of 8.6 times compared to transmitting single-precision measurements.