QVIO2: Quantized Map-Based Visual-Inertial Odometry

Energy-efficient visual-inertial motion tracking on SWAP-constrained edge devices (e.g., drones and AR glasses) is essential but challenging. Our previous work [1] introduced the first-of-its-kind quantized visual-inertial odometry (QVIO), utilizing either raw measurement quantization (zQVIO) or single-bit residual quantization (rQVIO). While QVIO has demonstrated significant data transfer reduction with competitive performance, it has limitations. Specifically, zQVIO directly quantizes raw measurements into multi-bit values, while requiring the ad-hoc inflation of measurement noise to account for quantization errors. On the other hand, rQVIO is limited to single-bit measurement with certain accuracy loss. This work introduces QVIO2 to address these issues. The proposed QVIO2 improves data quantization strategies and derives a Maximum A Posteriori (MAP) quantized estimator that rigorously handles both multi-bit and single-bit, raw and residual quantized measurements in a unified manner. These improvements lead to more communication-efficient and accurate systems. Additionally, we optimize the communication protocol to further reduce data transfer by eliminating unnecessary transmissions. Extensive numerical and experimental results demonstrate reduced communication requirements and improved accuracy. Compared to the previous QVIO system, zQVIO2 achieves the same accuracy with a 30 % reduction in data transfer, while rQVIO2 improves accuracy without increasing data communication. In real-world scenarios, our new zQVIO2 and rQVIO2 have demonstrated nearly no accuracy loss with only 4.6 bits and 3.5 bits of data communication, achieving compression rates of $7 \times$ and $9.1 \times$.