Low-cost sensor solutions provide a great opportunity for democratization of mapping among different communities including those working in digital twin areas. These sensors provide relatively dense point clouds with limited accuracy especially in the absence of GNSS. In this study, we provide two solutions for improving the accuracy of the final point clouds produced by iPhone-based LiDAR and images. One solution utilizes length observations as constraints in the network. Our tests showed that this solution improves the relative accuracy of the point cloud for applications such as culvert mapping where absolute accuracy is not of high necessity. In the other case, we studied the effect of using ultrasonic position systems to aid with the absolute accuracy improvement in the network. The results of this study are still under investigation and will be published in the full paper.