Abstract: Methods and systems for laser point clouds are described herein. The method and system may include receiving, at a computing device, lidar data indicative of an environment of a vehicle from a first lidar data source, where the lidar data includes a first plurality of data points indicative of locations of reflections from the environment and further includes a respective intensity for each data point. The method and system also include determining a first surface normal for at least a first data point of the first plurality of data points. The method and system further includes determining a first angle of incidence for the first data point based on the surface normal. Additionally, the method and system includes adjusting the intensity of the first data point based on the first angle of incidence to create a first adjusted intensity for the first data point.

Abstract: Example embodiments relate to light detection and ranging (lidar) devices having a light-guide manifold. An example lidar device includes a transmit subsystem. The transmit subsystem includes a light emitter. The transmit subsystem also includes a light-guide manifold optically coupled to the light emitter. Further, the transmit subsystem includes a telecentric lens assembly optically coupled to the light-guide manifold. The lidar device also includes a receive subsystem. The receive subsystem includes the telecentric lens assembly. The receive subsystem also includes an aperture plate having an aperture defined therein. The aperture plate is positioned at a focal plane of the telecentric lens assembly. Further, the receive subsystem includes a silicon photomultiplier (SiPM) positioned to receive light traveling through the aperture.

Abstract: Methods and systems for laser point clouds are described herein. The method and system may include receiving, at a computing device, lidar data indicative of an environment of a vehicle from a first lidar data source, where the lidar data includes a first plurality of data points indicative of locations of reflections from the environment and further includes a respective intensity for each data point. The method and system also include determining a first surface normal for at least a first data point of the first plurality of data points. The method and system further includes determining a first angle of incidence for the first data point based on the surface normal. Additionally, the method and system includes adjusting the intensity of the first data point based on the first angle of incidence to create a first adjusted intensity for the first data point.

Abstract: Examples described may enable provision of remote operation for a vehicle. An example method includes receiving sensor data from the vehicle. The method may further include determining a latency of a communication link. Additionally, the method may include providing at least one rendering to an operator based on the sensor data and the latency, where the rendering is based on a future location of the vehicle. The method may also include receiving an operator input. In response to the operator input, the method includes providing an instruction to the autonomous vehicle for execution by the autonomous vehicle based on the operator input via the communication link.

Abstract: A robotic system includes a dexterous robot and a controller. The robot includes a plurality of robotic joints, actuators for moving the joints, and sensors for measuring a characteristic of the joints, and for transmitting the characteristics as sensor signals. The controller receives the sensor signals, and is configured for executing instructions from memory, classifying the sensor signals into distinct classes via the state classification module, monitoring a system state of the robot using the classes, and controlling the robot in the execution of alternative work tasks based on the system state. A method for controlling the robot in the above system includes receiving the signals via the controller, classifying the signals using the state classification module, monitoring the present system state of the robot using the classes, and controlling the robot in the execution of alternative work tasks based on the present system state.

Abstract: A control interface for inputting data into a controller and/or controlling a robotic system is displayed on a human-to-machine interface device. The specific configuration of the control interface displayed is based upon the task to be performed, the capabilities of the robotic system, the capabilities of the human-to-machine interface device, and the level of expertise of the user. The specific configuration of the control interface is designed to optimize the interaction between the user and the robotic system based upon the above described criteria.

Abstract: A robotic system includes a dexterous robot and a controller. The robot includes a plurality of robotic joints, actuators for moving the joints, and sensors for measuring a characteristic of the joints, and for transmitting the characteristics as sensor signals. The controller receives the sensor signals, and is configured for executing instructions from memory, classifying the sensor signals into distinct classes via the state classification module, monitoring a system state of the robot using the classes, and controlling the robot in the execution of alternative work tasks based on the system state. A method for controlling the robot in the above system includes receiving the signals via the controller, classifying the signals using the state classification module, monitoring the present system state of the robot using the classes, and controlling the robot in the execution of alternative work tasks based on the present system state.