FPGA design of EKF block accelerator for 3D visual SLAM

- FPGA implementation of computing EKF gain and cross-covariance matrices is proposed.
- Exploiting cross-covariance matrix symmetry reduces computational and resource costs.
- EKF innovation matrix dimension allows for simple SA computational designs.
- Our innovation supports observations of 60 AHP landmarks in real time on Zynq-7020.
- Proposed hardware accelerator for EKF VO functionality is platform independent.

This paper deals with the evaluation of a dedicated architecture to be integrated into an embedded system typically mounted on a micro-aerial vehicle or on smart devices held by an operator. This system performs an Extended Kalman Filter (EKF) based visual odometry (VO) algorithm. An efficient hardware architecture conceived as a systolic array co-processor for EKF loop acceleration is presented. Due to severe limitations in terms of power consumption, real-time performance and physical characteristics of the system (i.e. compactness and weight), this algorithm is implemented entirely as a System On a programmable Chip (SoC) on the Zynq-7020 device. This heterogeneous (processor with reconfigurable hardware) platform consumes less power than a standard microprocessor and provides powerful parallel data processing capabilities: applying hardware/software (hw/sw) co-design allows real-time throughput with a very low power-per-feature rate.

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