In urban environments for delivery robots, particularly in areas such as campuses and towns, many custom features defy standard road semantic categorizations. Addressing this challenge, our paper introduces a method leveraging Salient Object Detection (SOD) to extract these unique features, employing them as pivotal factors for enhanced robot loop closure and localization. Traditional geometric feature-based localization is hampered by fluctuating illumination and appearance changes. Our preference for SOD over semantic segmentation sidesteps the intricacies of classifying a myriad of non-standardized urban features. To achieve consistent ground features, the Motion Compensate IPM (MC-IPM) technique is implemented, capitalizing on motion for distortion compensation and subsequently selecting the most pertinent salient ground features through moment computations. For thorough evaluation, we validated the saliency detection and localization performances to the real urban scenarios. Project page: https://sites.google.com/view/salient-ground-feature/home.

Recently, kernel-based tracking algorithms such as the mean shift tracking algorithm has been proposed, which use the information of color histogram together with some spatial information provided by the kernel. However, in spite of the fast speed, there exists an inherent instability problem which is due to the use of an isotropic kernel for spatiality and the use of the Bhattacharyya coefficient as the similarity function. In this paper, we will analyze how the use of the kernel and the Bhattacharyya coefficient can arouse the instability problem. Based on the analysis, we propose a tracking scheme that uses a new representation of the location of the target which is constrained by the color, the area, and the spatiality information of the target in a more stable way than the mean shift algorithm. With this representation, the target localization in the next frame can be achieved by a direct one step computation, and the tracking becomes stable, even in difficult situations such as low-rate-frame environment, and partial occlusion.

Among many image compression approaches, the discrete cosine transform (DCT) is widely used. However, dividing an image into small blocks prior to coding causes "blocking artifacts". A deblocking algorithm is proposed for DCT-based compressed images using anisotropic diffusion; it is derived from the ALM (Alvarez, Lions, Morel) diffusion model (Alvarez, L. et al., SIAM J. Numer. Anal., vol.29, no.3, p.845-66, 1992). It can control the diffusion rate in the normal direction of the edges using a "rate control parameter". It functions not only as an isotropic diffusion at block boundaries of smooth regions, but also as an anisotropic diffusion that diffuses the image only in the normal direction of the edges at edges or block boundaries of texture regions. The rate control parameters at block boundaries are chosen carefully to reduce discontinuities. To avoid oversmoothing of the texture region, a "speed parameter" is employed. The speed parameter makes the diffusion process slow at the texture region, while making it fast at the smooth region.