Maritime environments often present hazardous situations due to factors such as moving ships or buoys, which become obstacles under the influence of waves. In such challenging conditions, the ability to detect and track potentially hazardous objects is critical for the safe navigation of marine robots, but datasets capturing these scenarios remain limited. To address this limitation, we introduce a new multi-modal dataset that includes image and point-wise annotations of maritime obstacles. Our dataset provides detailed ground truth for obstacle detection and tracking, including objects as small as 10 × 10 pixels, which are crucial for maritime safety. To validate the dataset's effectiveness as a reliable benchmark, we conducted evaluations using various methodologies, including state-of-the-art (SOTA) techniques for object detection and tracking. These evaluations are expected to contribute to improving performance, particularly in the complex maritime environment. This represents the first demonstration of a dataset offering multi-modal annotations specifically tailored to maritime environments. Our dataset is available at https://github.com/sparolab/PoLaRIS.

Illumination variance is a major challenge for vision-based robotics. Most approaches focus on alleviating illumination changes in already captured images. Despite the large utility, camera attributes have been empirically determined to function in a highly passive manner, yielding vision algorithm failure under radical illumination variance. Recent studies have proposed exposure and gain control schemes that could maximize image information and eschew saturation. In this article, we propose a proactive control scheme for the camera's two dominant attributes-exposure time and gain control. Unlike existing approaches, we formulate this camera attribute control as an optimization problem in which the underlying function is not known a priori. We first define a new metric of the image regarding these two major attributes to include both image gradients and signal-to-noise ratio simultaneously. Based on this metric, we introduce a new formulation for this attribute control via Bayesian optimization (BO) and learn the environmental change from the captured image. During the control, to mitigate the burden of image acquisition and Bayesian optimization, images are synthesized using a camera response function and avoided the actual frame grab from the camera. The proposed method was validated in light-flickering indoor, outdoor near sunset, and indoor-outdoor transient environments where light changes rapidly, supporting 20-40 Hz frame rates.

This paper presents a localization and mapping algorithm that leverages a radar system in low-visibility environments. We aim to address disaster situations in which prior knowledge of a place is available from CAD or light detection and ranging (LiDAR) maps, but incoming visibility is severely limited. In smoky environments, typical sensors (e.g., cameras and LiDARs) fail to perform reliably due to the large particles in the air. Radars recently attracted attention for their robust perception in low-visibility environments; however, radar measurements' angular ambiguity and low resolution prevented the direct application to the simultaneous localization and mapping (SLAM) framework. In this paper, we propose registering radar measurements against a previously built dense LiDAR map for localization and applying radar-map refinement for mapping. Our proposed method overcomes the significant density discrepancy between LiDAR and radar with a density-independent point registration algorithm. We validate the proposed method in an environment containing dense fog.

Under- and oversaturation can cause severe image degradation in many vision-based robotic applications. To control camera exposure in dynamic lighting conditions, we introduce a novel metric for image information measure. Measuring an image gradient is typical when evaluating its level of image detail. However, emphasizing more informative pixels substantially improves the measure within an image. By using this entropy weighted image gradient, we introduce an optimal exposure value for vision-based approaches. Using this newly invented metric, we also propose an effective exposure control scheme that covers a wide range of light conditions. When evaluating the function (e.g., image frame grab) is expensive, the next best estimation needs to be carefully considered. Through Bayesian optimization, the algorithm can estimate the optimal exposure value with minimal cost. We validated the proposed image information measure and exposure control scheme via a series of thorough experiments using various exposure conditions.