A localization and mapping method is for localizing and mapping a moving apparatus in a moving process. The localization and mapping method includes an image capturing step, a feature point extracting step, a flag object identifying step, and a localizing and mapping step. The image capturing step includes capturing an image frame at a time point of a plurality of time points in the moving process by a camera unit. The flag object identifying step includes identifying whether the image frame includes a flag object among a plurality of the feature points in accordance with a flag database. The flag database includes a plurality of dynamic objects, and the flag object is corresponding to one of the dynamic objects. The localizing and mapping step includes performing localization and mapping in accordance with the image frames captured and the flag object thereof in the moving process.

The present technology relates to an information processing apparatus, an information processing method, and a program capable of making a path plan avoiding a crowd.

A cost map indicating a risk of passing through a region is generated using crowd information. The present technology can be applied to unmanned aerial vehicle (UAV) traffic management (UTM) and the like that control a UAV, for example.

An information processing apparatus includes: a shape information acquiring unit 204 configured to acquire shape information of a surrounding environment of a moving body measured by a sensor mounted in the moving body; a position and posture acquiring unit configured to acquire position and posture information of the sensor; a correction state acquiring unit configured to acquire a performance state relating to a process of correcting the position and posture information; a priority level determining unit configured to determine a priority level of an area for generating a map; and a map generating unit configured to generate the map on the basis of the shape information and the position and posture information acquired at the time of acquisition of the shape information, in which the map generating unit generates the map in order from an area of which the priority level is high in accordance with the performance state.

A device may receive emergency data, traffic data, network performance data, crime data, and gunshot data associated with a geographical area and may identify a location within the geographical area based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data. The device may determine, based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data for the location, a risk level for the location and may identify an autonomous vehicle based on the risk level, the traffic data, and the network performance data for the location. The device may determine a route for the autonomous vehicle to the location based on the traffic data and the network performance data for the location, and may perform actions based on the autonomous vehicle and the route.

Techniques for determining distortion in a map caused by measurement errors are discussed herein. For example, such techniques may include implementing a model to estimate map distortion between the map frame and the inertial frame. Data such as sensor data, map data, and vehicle state data may be input into the model. A map distortion value output from the model may be used to compensate vehicle operations in a local region by approximating the distortion as linearly varying about the region. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the trajectory.

According to an aspect of an embodiment, operations may comprise receiving a plurality of frame sets generated while navigating a local environment, receiving an occupancy map (OMap) representation of the local environment, for each of the plurality of frame sets, generating, using the OMap representation, one or more instances each comprising a spatial cluster of neighborhood 3D points generated from a 3D sensor scan of the local environment, and classifying each of the instances as dynamic or static, tracking instances classified as dynamic across the plurality of frame sets using a tracking algorithm, assigning a single instance ID to tracked instances classified as dynamic across the plurality of frame sets, estimating a bounding box for each of the instances in each of the plurality of frame sets, and employing the instances as ground truth data in a training of one or more deep learning classifiers.

A digital map of a transport vehicle for transporting vehicles includes one or more digital reference locations that are respectively associated with real reference locations of the transport vehicle. A method includes: ascertaining respective reference coordinates of the real reference locations relative to a reference coordinate system, so that the ascertained reference coordinates are associated with the corresponding digital reference locations; deriving respective coordinates of further digital locations of the digital map from the reference coordinates of the reference locations based on a derivation protocol, so that coordinates relative to the reference coordinate system are respectively associated with the further digital locations, so that an edited digital map of the transport vehicle is created, in which map coordinates relative to the reference coordinate system are associated with the digital locations.

The present disclosure discloses a path planning method and device and a mobile device. The method comprises: collecting environmental information in a viewing angle by a sensor of a mobile device, processing the environmental information by using an SLAM algorithm, and constructing a grid map; dividing the grid map to obtain a plurality of pixel blocks, using an area constituted of pixel blocks not occupied by obstacles as a search area for path planning, and obtaining a processed grid map; determining reference points by using pixel points in the search area, and deploying topological points on the processed grid map according to the reference point determined and constructing a topological map; and calculating an optimal path from a starting point to a preset target point by using a predetermined algorithm according to the topological map constructed. The present disclosure improves path planning efficiency and saves storage resources.

An apparatus receives a plurality of instances of map update data during a time window. Each instance is associated with a respective geographic region of a plurality of defined geographic regions. The apparatus determines, for the time window, region rankings for the plurality of defined geographic regions. A region ranking is determined based on a volume measure, a measure of map update activity, a measure of inaccurate activity, a general priority weight, and/or an interest density corresponding to the respective geographic region. The apparatus determines, for the time window, respective ranking ranges for a plurality of data buckets and assigns respective instance rankings to the instances of map update data. The instances are assigned to data buckets based on the instance rankings and ranking ranges. The apparatus ingests the instances based on the respective data buckets to cause a digital map maintained by the apparatus to be updated.

A computer-implemented method of refining a high definition (HD) map of a parking lot for autonomous vehicle parking (AVP) is disclosed. The method including: a vehicle navigating in the parking lot using the HD map; the vehicle using one or more sensors to scan for objects in the parking lot; the vehicle augmenting the HD map using a simultaneous localization & mapping SLAM method; adding objects detected by the one or more sensors to the HD map; and contributing towards improving a learning score of the HD map.