Intel Corporation

Jose I Parra Vilchis

David Gomez Gutierrez

Rafael De La Guardia Gonzalez

Leobardo E Campos Macias

Rodrigo Aldana Lopez

Julio Cesar Zamora Esquivel

Anthony Kyung Guzman Leguel

Hector A Cordourier Maruri

Jonathan J Huang

Jose Rodrigo Camacho Perez

Paulo Lopez Meyer

Willem M Beltman

David Arditti Ilitzky

Bradley Alan Jackson

Joshua Triska

Carl S Marshall

David W Browning

David Israel Gonzalez Aguirre

Daniel Pohl

Kevin Joseph Doran

Gabriel C Cox

Rafael De La Guardia González

Jan Willem Vervoorst

Andreas Hippelein

A method for calculating a time to contact of an autonomous vehicle, the method comprising: obtaining a plurality of event data an image, wherein the event data is associated with a pixel associated with a change in light intensity; determining a reference signal frequency associated with a transmitted light; identifying a select event data from the plurality of event data, wherein the light frequency associated with the select event data is substantially the same as the reference signal frequency; determining an object based on the select event data, wherein the object is fully enclosed by a bounding box comprising coordinates of a rectangular border; calculating a distance between a set of coordinates of the bounding box closest to the autonomous vehicle and the autonomous vehicle; and calculating the time to contact between the set of coordinates of the bounding box and the autonomous vehicle.

Methods and devices for detecting objects and calculating a time to contact in autonomous driving systems

Techniques are disclosed to facilitate, in autonomous vehicles, the robust detection and classification of objects in a scene using a static sensors in conjunction with event-based sensors. A trained system architecture may be implemented, and the fusion of both sensors thus allows for the consideration of scenes with overexposure, scenes with underexposure, as well as scenes in which there is no movement. In doing so, the autonomous vehicle may detect and classify objects in conditions in which each sensor, if operating separately, would not otherwise be able to classify (or classify with high uncertainty) due to the sensing environment.

Robust object detection and classification using static-based cameras and events-based cameras

Systems and techniques for an architecture for contextual memories in map representation for 3D reconstruction and navigation are described herein. In an example, a system for contextual memory mapping is adapted to receive a data set of physical world sensor readings. The system may be further adapted to generate voxel data from the data set, the voxel data includes voxel coordinates and a physical world occupancy indicator. The system may be further adapted to select a block of addresses in the memory to store the voxel data. The system may be further adapted to generate a hash map to map voxel coordinates to memory locations in the block of addresses, the voxel coordinates having a contextual relationship that is maintained by the hash map. The system may be further adapted to store the voxel data at memory addresses based on the hash map.

Architecture for contextual memories in map representation for 3D reconstruction and navigation

An apparatus of an autonomous device comprises one or more state estimators to estimate one or more states of the autonomous device, wherein the one or more state estimators are to generate one or more derivatives of translational measurements, orientation measurements, reference translational values, and reference orientation values, and one or more controllers to receive an output from the one or more state estimators to provide control signals to control the autonomous device. The one or more state estimators include a hardware differentiator to generate the one or more derivatives.

Hardware accelerator for online estimation

Techniques are disclosed to facilitate cooperative mapping for safe and efficient trajectory planning and collision avoidance by allowing nearby agents to share contextual information. The described techniques also function to extend the mapping range of a single agent by leveraging observations made by multiple agents. Furthermore, the techniques as described herein function to reduce uncertainty in trajectory planning by allowing agents to 'see' behind occlusions, thus taking advantage of observations made by neighboring agents from different points of view. An efficient hardware implementation of the system is also presented that leverages the methodologies as discussed herein.

Cooperative mapping for autonomous vehicles, robots or multi-agent systems

Methods and apparatus for preventing collisions between drones based on drone-to-drone acoustic communications are disclosed. A control system of a first drone includes a route manager to command the first drone to track a first route. The control system further includes a plurality of acoustic sensors arranged on the first drone to detect acoustic signals. The control system further includes a collision prevention engine to determine a second route based on acoustic signals detected by the acoustic sensors, the acoustic signals being received from a second drone while the first drone is tracking the first route. The second route differs from the first route to prevent a drone-to-drone collision between the first drone and the second drone. The collision prevention engine is to cause the route manager to command the first drone to track the second route instead of the first route.

Methods and apparatus for preventing collisions between drones based on drone-to-drone acoustic communications

System and techniques for drone obstacle avoidance using real-time wind estimation are described herein. A wind metric is measures at a first drone and communicated to a second drone. In response to receiving the wind metric, a flight plan of the second drone is modified based on the wind metric.

Drone obstacle avoidance using real-time wind estimation

Various systems and methods for drone path planning are described herein. A system for drone path planning for a drone, the system to perform the operations: storing a current location of the drone as a current waypoint in a set of waypoints, the set of waypoints to include the current waypoint and a previous waypoint; detecting a current set of frontiers from the current location; storing the current set of frontiers in a list of active goals, the current set of frontiers associated with the current location; determining whether a target is discovered in the current set of frontiers; exploring the current set of frontiers to attempt to find the target; and exploring a previous set of frontiers from the list of active goals, when exploring the current set of frontiers does not, discover the target, the previous set of frontiers associated with the previous waypoint.

Drone path planning

In one embodiment, a distributed oscillator computes a first error value based on a first state value for the distributed oscillator and first state values for other distributed oscillators, and computes a second error value based on a second state value for the distributed oscillator and second state values for the other distributed oscillators. The distributed oscillator computes a new first state value for the distributed oscillator based on the first error value, the first state value for the distributed oscillator, and the second state value for the distributed oscillator, and computes a new second state value for the distributed oscillator based on the second error value, the first state value for the distributed oscillator, and the second state value for the distributed oscillator. The distributed oscillator transmits the new first state value and the new second state value to the other distributed oscillators.

Decentralized synchronization of multiple agents

Systems and methods may include a lift drone and a carrier drone to convey a payload. The lift drone may vertically lift the payload, alone or with the carrier drone, to a transfer location. The carrier drone may receive control of the payload at the transfer location, such as by receiving physical transfer of the payload, taking over conveyance of the payload from the lift drone, or the like. The lift drone may remain coupled with the payload or the carrier drone or may decouple after transfer.

Drone based delivery system

Systems and methods include receiving, values of one or more first external time variables from a first external node and values of one or more second external time variables from a second external node. The values of one or more local time variables of the local node are adjusted based at least upon the values of the one or more first external time variables and the values of the one or more second external time variables.

Time-synchronizing a group of nodes

Methods, apparatus, systems and articles of manufacture are disclosed herein including a monitoring system for a building including an in-wall guide for a drone, the in-wall guide extending behind at least one wall from a first location to a second location and a power delivery circuit in, or adjacent, the in-wall guide.

Apparatus and system for building monitoring

Methods and apparatus for drone collision avoidance. Processing circuitry of a drone extracts information from an encoded image captured by a detection device with a field view overlapping the encoded image. Based on the extracted information a determination is made whether a collision will occur on the flight trajectory of the drone with an external source. The flight trajectory of the drone is then altered to avoid a collision.

Projection-based cooperative collision avoidance

An example unmanned aerial vehicle traffic signal and related methods are disclosed. The example unmanned aerial vehicle includes a housing, a rotor, a motor, a sensor, a traffic signal, and a processor. The rotor is to lift the housing off ground. The motor is to drive the rotor. The sensor is to monitor traffic. The traffic signal is carried by the housing. The processor is to control the traffic signal based on the traffic monitored by the sensor.

Unmanned aerial vehicle traffic signals and related methods

Methods, systems, and apparatus for audio noise reduction from a drone are disclosed. An example apparatus includes an acoustic sensor to gather acoustic data and at least one rotational motion sensor to gather rotational motion data of a first rotor and second rotational motion data of a second rotor. The example apparatus also includes an analyzer to identify a first filter that matches the first rotational motion data and identify a second filter that matches the second rotational motion data. The analyzer also is to filter the acoustic data into filtered acoustic data with the first identified filter and the second identified filter and generate an audio signal based on the filtered acoustic data.

Growing community of inventors

Gaudalajara, Mexico

Jose I Parra Vilchis