Frequency-modulated continuous-wave (FMCW) scanning radar has emerged as an alternative to spinning LiDAR for state estimation on mobile robots. Radar's longer wavelength is less affected by small particulates, providing operational advantages in challenging environments such as dust, smoke, and fog. This paper presents Radar Teach and Repeat (RT&R): a full-stack radar system for long-term off-road robot autonomy. RT&R can drive routes reliably in off-road cluttered areas without any GPS. We benchmark the radar system's closed-loop path-tracking performance and compare it to its 3D LiDAR counterpart. 11.8 km of autonomous driving was completed without interventions using only radar and gyro for navigation. RT&R was evaluated on four different routes with progressively less structured scene geometry. RT&R achieved lateral path-tracking root mean squared errors (RMSE) of 5.6 cm, 7.5 cm, and 12.1 cm as the routes became more challenging. These RMSE values are less than half of the width of one tire (24 cm) on our robot testing platform. These same routes have worst-case errors of 21.7 cm, 24.0 cm, and 43.8 cm. We conclude that radar is a viable alternative to LiDAR for long-term autonomy in challenging off-road scenarios. The implementation of RT&R is open-source and available at: https://github.com/utiasASRL/vtr3.

Classic McEliece, with a remarkably stable security level, has been selected as one of the four key-establishment algorithms in the fourth-round evaluation of the post-quantum cryptography (PQC) standardization process of national institute of standards and technology (NIST). However, its memory-intensive and time-consuming key generation poses an obstacle to widespread use. In this paper, we propose a fast hardware implementation of the key generation incorporating several architectural optimizations. For the Gaussian elimination, we optimize the scheduling of computing resources and the memory access process and present a high-performance and flexible systemizer with multiple low fan-out systolic arrays. Besides, an algorithmic-level parallelized design for entry generation and Gaussian elimination is proposed to reduce the redundant computation time. A compact entry generator with a multi-level feedback mechanism and a 2-D high-speed FFT module facilitates continuous streaming the generated entries into the systemizer.FPGA implementation results show that our designs for the key generation improve time-area efficiency by 11.9% to 43.2% compared to the state-of-the-arts. Moreover, compared to the hardware implementations for the key generation of the other two quasi-cyclic code-based PQC algorithms, ours for Classic McEliece based on the random code achieves close to or better results in several metrics.

Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has emerged as a low-cost and scalable indoor positioning solution. However, in cluttered environments, the performance of UWB TDOA-based localization deteriorates due to the biased and non-Gaussian noise distributions induced by obstacles. In this work, we present a bi-level optimization-based joint localization and noise model learning algorithm to address this problem. In particular, we use a Gaussian mixture model (GMM) to approximate the measurement noise distribution. We explicitly incorporate the estimated state's uncertainty into the GMM noise model learning, referred to as uncertainty-aware GMM, to improve both noise modeling and localization performance. We first evaluate the GMM noise model learning and localization performance in numerous simulation scenarios. We then demonstrate the effectiveness of our algorithm in extensive real-world experiments using two different cluttered environments. We show that our algorithm provides accurate position estimates with low-cost UWB sensors, no prior knowledge about the obstacles in the space, and a significant amount of UWB radios occluded.

Extended integrated interleaved (EII) codes, as the versatile alternative to locally recoverable codes (LRCs), show great potential in distributed storage systems, in which the output bit-error-rate (BER) below 10−15is usually demanded. However, it is time-consuming to reach such a low BER through normal software simulation, which brings inconvenience to the code construction. To solve the above problem, this work presents an analysis method to evaluate the decoding performance of EII codes, and no simulation is required. Numerical results show that the estimated frame-error-rate (FER) matches well with the simulated FER, so does the BER. Moreover, the failure probability of each decoding stage can be predicted accurately. Therefore, we can dig deep into the decoding behavior of each stage, which guides the adjustment of redundancy distribution, improving the error correction performance. Finally, the theoretical analysis for regular EII codes is simplified to reduce calculations.