LiDAR and camera are two of the most common sensors used in the fields of robot perception, autonomous driving, augmented reality, and virtual reality, where these sensors are widely used to perform various tasks such as odometry estimation and 3D reconstruction. Fusing the information from these two sensors can significantly increase the robustness and accuracy of these perception tasks. The extrinsic calibration between cameras and LiDAR is a fundamental prerequisite for multimodal systems. Recently, extensive studies have been conducted on the calibration of extrinsic parameters. Although several calibration methods facilitate sensor fusion, a comprehensive summary for researchers and, especially, non-expert users is lacking. Thus, we present an overview of extrinsic calibration and discuss diverse calibration methods from the perspective of calibration system design. Based on the calibration information sources, this study classifies these methods as target-based or targetless. For each type of calibration method, further classification was performed according to the diverse types of features or constraints used in the calibration process, and their detailed implementations and key characteristics were introduced. Thereafter, calibration-accuracy evaluation methods are presented. Finally, we comprehensively compare the advantages and disadvantages of each calibration method and suggest directions for practical applications and future research.

Vibrotactile feedback systems are widely used in assistive technology, wearable devices, and virtual environments to deliver precise tactile information. The timing of interstimulus intervals (ISIs) plays a critical role in determining how accurately users perceive and interpret vibrotactile patterns. The optimal use of ISIs can increase the effectiveness of these systems, improve user interaction, and enable reliable, intuitive feedback in diverse applications. We examined how different interstimulus intervals ISIs impact the accuracy of vibrotactile pattern recognition. Participants wore a forearm-mounted device with six voice coil actuators arranged in a 3 × 2 grid, delivering Braille-based vibrotactile patterns sequentially at ISIs ranging from 10 to 2500 ms. Eight participants performed identification tasks involving Icelandic Braille patterns categorized as either short (2–3 actuators) or long (4–5 actuators). A repeated measures ANOVA was conducted to assess the effects of ISI, pattern type, and practice (across two testing blocks) on pattern recognition accuracy. For short patterns, accuracy was highest (92%–98%) at ISIs of 50–700 ms, with peak performance at 300 ms. For long patterns, accuracy reached 86%–94% at ISIs of 100–500 ms, peaking at 400 ms. Participants were more accurate with short patterns, and performance improved significantly over time for both short and long patterns, highlighting the importance of training for vibrotactile pattern recognition. These results underscore the importance of careful selection of ISIs in vibrotactile feedback systems for accurate pattern identification. The findings provide valuable insights for conveying tactile information using wearable devices, contributing to better tactile feedback and performance in applications requiring precise vibrotactile information delivery.