Career Adaptability and Career Success

In this study, we contribute to the ongoing incremental validation efforts of the Career Adapt-Abilities Scale (CAAS). Using primary personality and cognition data from 164 German athletes in vocational careers, we intended to replicate Zacher’s (2014) seminal work in an alternative Western environment while also extending it in two significant ways: first, by adding two components of cognitive ability, and second, by introducing an alternative outcome variable – objective career success. In line with Zacher, we observe a significant role of career adaptability in predicting subjective career success. However, we also note that this initially robust relationship stems from a different psychosocial resource than expected. Interestingly, employing CAAS seems not to possess further incremental validity when predicting objective career success.

Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target

Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.