Large-scale 3D scene reconstruction is an important task in autonomous driving and other robotics applications as having an accurate representation of the environment is necessary to safely interact with it. Reconstructions are used for numerous tasks ranging from localization and mapping to planning. In robotics, volumetric depth fusion is the method of choice for indoor applications since the emergence of commodity RGB-D cameras due to its robustness and high reconstruction quality. In this work we present an approach for volumetric depth fusion using LiDAR sensors as they are common on most autonomous cars. We present a framework for large-scale mapping of urban areas considering loop closures. Our method creates a meshed representation of an urban area from recordings over a distance of 3.7km with a high level of detail on consumer graphics hardware in several minutes. The whole process is fully automated and does not need any user interference. We quantitatively evaluate our results from a real world application. Also, we investigate the effects of the sensor model that we assume on reconstruction quality by using synthetic data.

Many approaches for camera and LiDAR calibration are presented in literature but none of them estimates all intrinsic and extrinsic parameters simultaneously and therefore optimally in a probabilistic sense.In this work, we present a method to simultaneously estimate intrinsic and extrinsic parameters of cameras and LiDARs in a unified problem. We derive a probabilistic formulation that enables flawless integration of different measurement types without hand-tuned weights. An arbitrary number of cameras and LiDARs can be calibrated simultaneously. Measurements are not required to be time-synchronized. The method is designed to work with any camera model.In evaluation, we show that additional LiDAR measurements significantly improve intrinsic camera calibration. Further, we show on real data that our method achieves state-of-the-art calibration precision with high reliability.

Highly accurate localization with very limited amount of memory and computational power is one of the big challenges for next generation series cars. We propose localization based on geometric primitives which are compact in representation and further valuable for other tasks like planning and behavior generation. The primitives lack distinctive signature which makes association between detections and map elements highly ambiguous. We resolve ambiguities early in the pipeline by online building up a local map which is key to runtime efficiency. Further, we introduce a new framework to fuse association and odometry measurements based on robust pose graph optimization.We evaluate our localization framework on over 30 min of data recorded in urban scenarios. Our map is memory efficient with less than 8 kB/km and we achieve high localization accuracy with a mean position error of less than 10 cm and a mean yaw angle error of less than 0. 25° at a localization update rate of 50Hz.

In this work we present a novel approach for multi-sensor calibration that significantly outperforms current state-of-the-art. We introduce a new spherical calibration target which has major benefits over existing targets. Those are subresolution detection accuracy in both camera and depth sensor, view invariance and applicability to a wider range of sensor setups than current approaches. With our method a single person achieves high quality calibration in less than a minute. No preparations for setting up the environment for calibration is needed. Our method is fast, easy to use and fully automatic. We evaluate our method in simulation and show high accuracy with an error of less than 3mm in translation and 0.1 0 in rotation on real data.

We present an improved model for MRF-based depth upsampling, guided by image-as well as 3D surface normal features. By exploiting the underlying camera model we define a novel regularization term that implicitly evaluates the planarity of arbitrary oriented surfaces. Our method improves upsampling quality in scenes composed of predominantly planar surfaces, such as urban areas. We use a synthetic dataset to demonstrate that our approach outperforms recent methods that implement distance-based regularization terms. Finally, we validate our approach for mapping applications on our experimental vehicle.