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.

As the importance of ideas as the basis of innovation is growing bigger, interest in ways to effectively generate creative ideas is increasing as well. This study focused on figuring out the effective methods of instruction used for generating ideas since conventional instructions asking for good ideas can hurt ideation. As an alternative to the conventional instructions, we proposed the Two-step method which gives instruction to generate random ideas that make no sense first and then requires people to revise generated ideas so as to make a lot of creative ideas. We conducted an experiment to find out whether the proposed Two-step method is more effective in generating creative ideas than the method that asks for good ideas or the method that requests random ideas that make no sense. Results show that the Two-step method can help people generate more creative ideas than other methods.