Aspects concern a method for correcting errors in map data comprising obtaining map data specifying travel possibilities between locations, generating a routing graph from the map data by assigning a vertex to each location and an edge from one vertex to another vertex if the map data specifies that it is possible to travel from the location to which the first vertex is assigned to the location to which the second vertex is assigned, identifying the largest strongly connected component of the routing graph, identifying one or more further strongly connected components of the routing graph, detecting errors in the map data by identifying travel possibilities that are not in the map data which lead to missing connections between the one or more further strongly connected components of the routing graph and the largest strongly connected component of the routing graph and amending the map data by the identified travel possibilities.

A display control method for easily and intuitively recognizing a height of a flight path of a flight object is provided. The display control method is used to control the display of the flight path of the flight object and includes the following steps: obtaining a two-dimensional (2D) map including longitude and latitude information; obtaining the flight path of the flight object in three-dimensional (3D) space; and determining a display mode of the flight path superimposed and displayed on the 2D map based on a height of the flight path.

In an apparatus including a personal monitoring device which includes a microphone, speaker, camera, memory or database, global positioning device, controller, and transmitter, the improvement including a wearable device configured to include at least one of more components of the personal monitoring device on, with, or within, the wearable device, and a smoke detector, the fire detector, or the carbon monoxide (CO) detector. The microphone, speaker, or camera is located on an exterior of the wearable device. The smoke detector, fire detector, or carbon monoxide (CO) detector, is located on an exterior of the wearable device. The smoke detector, fire detector, or carbon monoxide (CO) detector, detects a presence of smoke, a fire, or a presence of carbon monoxide, and the speaker emits an alarm, a siren blare, or an audible and verbal message. The transmitter transmits a message to a computer or communication device.

A system includes a first vehicle configured to generate first data, and an infrastructure configured to acquire high-definition (HD) map data, to receive the first data, and to generate first electronic horizon data of a specified region in terms of the first vehicle based on the HD map data and the first data and to transmit the first electronic horizon data to the first vehicle when receiving a request signal from the first vehicle.

The present disclosure relates to a vehicular electronic device including a power supply configured to supply power, an interface configured to receive HD map data on a specific area from a server through a communication device and receive data on driving condition information of a vehicle, and a processor configured to continuously generate electronic horizon data on a specific area based on the high-definition (HD) map data in the state of receiving the power and to set a geographical range of the electronic horizon data based on data on the driving condition information.

A feature mapping computer system configured to (i) receive a localized image including a photo depicting a driving environment and location data associated with the photo, (ii) identify, using an image recognition module, a roadway feature depicted in the photo, (iii) generate, using a photogrammetry module, a point cloud based upon the photo and the location data, wherein the point cloud comprises a set of data points representing the driving environment in a three dimensional (“3D”) space, (iv) localize the point cloud by assigning a location to the point cloud based upon the location data, and (v) generate an enhanced base map that includes a roadway feature.

The present disclosure provides a method and a system for modelling a poor texture tunnel based on a vision-lidar coupling. The method includes: obtaining point cloud information collected by a depth camera, laser information collected by a lidar, and motion information of an unmanned aerial vehicle (UAV); generating a raster map based on the laser information, and obtaining pose information of the UAV based on the motion information; obtaining a map model through fusing the point cloud information, the raster map, and the pose information by a Bayesian fusion method; and correcting a latest map model by feature matching based on a previous map model.

A method (400) and a control unit (210) for ground bearing capacity analysis. The method (400) steps include determining (401) a shape of the terrain segment (130) ahead of a vehicle (100), based on sensor measurements; predicting (402) a distance between a sensor (120) of the vehicle (100) and the ground (110) at the terrain segment (130), before the vehicle (100) moves into the terrain segment (130); measuring (403) the distance between the sensor (120) of the vehicle (100) and the ground (110) when the vehicle (100) has moved into the terrain segment (130); and determining (404) that the terrain segment (130) is to be avoided due to insufficient bearing capacity when the predicted (402) distance between the sensor (120) and the ground (110) exceeds the measured (403) distance between the sensor (120) and the ground (110). Also, a method (600) and control unit (210) for route planning of the vehicle (100) are described.

Among other things, we describe techniques for detecting objects in the environment surrounding a vehicle. A computer system is configured to receive a set of measurements from a sensor of a vehicle. The set of measurements includes a plurality of data points that represent a plurality of objects in a 3D space surrounding the vehicle. The system divides the 3D space into a plurality of pillars. The system then assigns each data point of the plurality of data points to a pillar in the plurality of pillars. The system generates a pseudo-image based on the plurality of pillars. The pseudo-image includes, for each pillar of the plurality of pillars, a corresponding feature representation of data points assigned to the pillar. The system detects the plurality of objects based on an analysis of the pseudo-image. The system then operates the vehicle based upon the detecting of the objects.

According to an aspect of an embodiment, operations may comprise obtaining a pose graph that comprises a plurality of nodes. The operations may also comprise dividing the pose graph into a plurality of pose subgraphs, each pose subgraph comprising one or more respective pose subgraph interior nodes and one or more respective pose subgraph boundary nodes. The operations may also comprise generating one or more boundary subgraphs based on the plurality of pose subgraphs, each of the one or more boundary subgraphs comprising one or more respective boundary subgraph boundary nodes and comprising one or more respective boundary subgraph interior nodes. The operations may also comprise obtaining an optimized pose graph by performing a pose graph optimization. The pose graph optimization may comprise performing a pose subgraph optimization of the plurality of pose subgraphs and performing a boundary subgraph optimization of the plurality of boundary subgraphs.