Lane-level maps provide crucial detail to autonomous vehicle perception and decision making systems. Many common HD maps require human annotators to painstakingly label attributes such as lane geometry, connectivity, and speed limit. In this paper, we propose a method to derive these attributes automatically. Our method uses lane features and previous vehicle experience to generate potentials in a rectified raster map. We then infer the driving lanes within a Markov random field. Moreover, the lane topologies are extracted and represented by parsing the roadway into different segments based on the number of driving lanes and generating lane transitions between adjacent road segments. Lane attributes, such as speed limit and stop locations, are inferred from statistical analysis of previous vehicle experiences. Our approach is supported by experiments demonstrating effective automatic lane map generation on real-world roads.

Determining the rigid-body transformation be-tween 2D image data and 3D point cloud data has applications for mobile robotics including sensor calibration and localizing into a prior map. Common approaches to 2D-3D registration use least-squares solvers assuming known associations often provided by heuristic front-ends, or iterative nearest-neighbor. We present a linear line-based 2D-3D registration algorithm formulated as a mixed-integer program to simultaneously solve for the correct transformation and data association. Our formulation is explicitly formulated to handle outliers, by modeling associations as integer variables. Additionally, we can constrain the registration to SE(2) to improve runtime and accuracy. We evaluate this search over multiple real-world data sets demonstrating adaptability to scene variation.

Sparse feature-based maps provide a compact representation of the environment that admit efficient algorithms, for example simultaneous localization and mapping. These representations typically assume a static world and therefore contain static map features. However, since the world contains dynamic elements, determining when map features no longer correspond to the environment is essential for long-term utility. This work develops a feature-based model of the environment which evolves over time through feature persistence. Moreover, we augment the state-of-the-art sparse mapping model with a correlative structure that captures spatio-temporal properties, e.g. that nearby features frequently have similar persistence. We show that such relationships, typically addressed through an ad hoc formalism focusing only on feature repeatability, are crucial to evaluate through a probabilistically principled approach. The joint posterior over feature persistence can be computed efficiently and used to improve online data association decisions for localization. The proposed algorithms are validated in numerical simulation and using publicly available data sets.

Many autonomous systems require the ability to perceive and understand motion in a dynamic environment. We present a novel algorithm that estimates this motion from raw LIDAR data in real-time without the need for segmentation or model-based tracking. The sensor data is first used to construct an occupancy grid. The foreground is then extracted via a learned background filter. Using the filtered occupancy grid, raw scene flow between successive scans is computed. Finally, we incorporate these measurements in a filtering framework to estimate temporal scene flow. We evaluate our method on the KITTI dataset.

This paper reports on a fast multiresolution scan matcher for vehicle localization in urban environments for self-driving cars. State-of-the-art approaches to vehicle localization rely on observing road surface reflectivity with a three-dimensional (3D) light detection and ranging (LIDAR) scanner to achieve centimeter-level accuracy. However, these approaches can often fail when faced with adverse weather conditions that obscure the view of the road paint (e.g., puddles and snowdrifts) or poor road surface texture. We propose a new scan matching algorithm that leverages Gaussian mixture maps to exploit the structure in the environment; these maps are a collection of Gaussian mixtures over the z-height distribution. We achieve real-time performance by developing a novel branch-and-bound, multiresolution approach that makes use of rasterized lookup tables of these Gaussian mixtures. Results are shown on two datasets that are 3.0 km: a standard trajectory and another under adverse weather conditions.

This paper reports on the problem of map-based visual localization in urban environments for autonomous vehicles. Self-driving cars have become a reality on roadways and are going to be a consumer product in the near future. One of the most significant road-blocks to autonomous vehicles is the prohibitive cost of the sensor suites necessary for localization. The most common sensor on these platforms, a three-dimensional (3D) light detection and ranging (LIDAR) scanner, generates dense point clouds with measures of surface reflectivity-which other state-of-the-art localization methods have shown are capable of centimeter-level accuracy. Alternatively, we seek to obtain comparable localization accuracy with significantly cheaper, commodity cameras. We propose to localize a single monocular camera within a 3D prior ground-map, generated by a survey vehicle equipped with 3D LIDAR scanners. To do so, we exploit a graphics processing unit to generate several synthetic views of our belief environment. We then seek to maximize the normalized mutual information between our real camera measurements and these synthetic views. Results are shown for two different datasets, a 3.0 km and a 1.5 km trajectory, where we also compare against the state-of-the-art in LIDAR map-based localization.