Abstract: A mobility device configurable in a first walk configuration and a second stair-climb configuration, including a frame with movable front leg portions, a leg-actuation system connected to the front leg portions and a safety-control device mounted to the frame. The safety-control device includes a configuration selector, and a lever connected to the leg-actuation system and configured to actuate the cable actuation system so as to cause the first and second movable leg portions to move from a first position to a second position. The lever remains locked in a first lever position such that the lever cannot be moved unless the configuration selector is in a depressed position, thereby preventing inadvertent movement of the leg portion.

Abstract: A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may have an effective field of view created by individual sensors with overlapping fields of view. The sensor pod system may include sensors of different types and modalities. Sensor pods of the sensor pod system may be modularly installed on a vehicle, for example, an autonomous vehicle and collect and provide data of the environment during operation of the vehicle.

Abstract: Techniques are discussed for modifying map elements associated with map data. Map data can include three-dimensional data (e.g., LIDAR data) representing an environment, while map elements can be associated with the map data to identify locations and semantic information associated with an environment, such as regions that correspond to driving lanes or crosswalks. A trajectory associated with the map data can be updated, such as when aligning one or more trajectories in response to a loop closure, updated calibration, etc. The transformation between a trajectory and an updated trajectory can be applied to map elements to warp the map elements so that they correspond to the updated map data, thereby providing automatic and accurate techniques for updating map elements associated with map data.

Abstract: A controller may identify a plurality of sets of potential parameters associated with operation of an engine, wherein each set of potential parameters includes a potential setting of a turbine of a variable geometry turbocharger (VGT) of the engine and a potential setting of an intake throttle valve (ITV) of the engine. The controller may determine, based on the plurality of sets of potential parameters, a plurality of sets of predicted values, wherein each set of predicted values includes a predicted outlet pressure of a compressor of the VGT and a predicted temperature of an exhaust gas of the engine. The controller may determine respective scores of the plurality of sets of predicted values, and may select, based on the respective scores, a particular set of predicted values of the plurality of sets of predicted values. The controller may thereby control the turbine of the VGT and the ITV.

Abstract: Techniques for determining a probability that a first sensor is miscalibrated with respect a second sensor are discussed herein. For example, a computing device may receive calibrated extrinsics of a camera to a lidar, determine a plurality of sets of perturbed extrinsics based on the calibrated extrinsics, determine respective costs for perturbed extrinsics of the plurality of sets of perturbed extrinsics based on image data captured by the camera, the plurality of sets of perturbed extrinsics, and lidar data captured by the lidar, and determine a local maxima score for the calibrated extrinsics based at least in part on the respective costs for the perturbed extrinsics of the plurality of sets of perturbed extrinsics and a cost of the calibrated extrinsics. The computing device may then determine a probability that the camera is miscalibrated based on a Bayes probability and the local maxima score.

Abstract: Techniques for determining a safety metric associated with a vehicle controller are discussed herein. To validate safe operation of a system, a simulation may be executed including determining a relative location of a simulated object within the simulation with respect to a location of a simulated vehicle, determining, based on the relative location of the simulated object, an adjusted location of the simulated object within the simulation, controlling, by the autonomous vehicle controller and based on the relative location of the simulated object, the simulated vehicle to follow a trajectory within the simulation, and performing a collision check between the simulated vehicle and the simulated object at the adjusted location. The safety metric associated with the autonomous vehicle controller may then be determined based at least in part an outcome of the collision check.

Abstract: A controller may identify a plurality of sets of potential parameters associated with operation of an engine, wherein each set of potential parameters includes a potential setting of a turbine of a variable geometry turbocharger (VGT) of the engine and a potential setting of an intake throttle valve (ITV) of the engine. The controller may determine, based on the plurality of sets of potential parameters, a plurality of sets of predicted values, wherein each set of predicted values includes a predicted outlet pressure of a compressor of the VGT and a predicted temperature of an exhaust gas of the engine. The controller may determine respective scores of the plurality of sets of predicted values, and may select, based on the respective scores, a particular set of predicted values of the plurality of sets of predicted values. The controller may thereby control the turbine of the VGT and the ITV.

Abstract: Techniques for integrating sensor data into a scene or map based on statistical data of captured environmental data are discussed herein. The data may be stored as a multi-resolution voxel space and the techniques may comprise first applying a pre-alignment or localization technique prior to fully integrating the sensor data.

Abstract: Systems and methods for calibrating multiple inertial measurement units on a system include calibrating a first of the inertial measurement units relative to the system using a first calibration model, and calibrating the remaining inertial measurement unit(s) relative to the first inertial measurement unit using a second calibration model. The calibration of the remaining inertial measurement unit(s) to the first inertial measurement unit can be based on a rigid body model by aligning a rotational velocity of the first inertial measurement unit with a rotational velocity of the remaining inertial measurement unit(s).

Abstract: Techniques described are related to determining when a discrepancy between data of multiple sensors (e.g., IMUs) might be attributable to a sensor error, as opposed to operating conditions, such as sensor bias or noise. For example, the sensor data is passed through one or more filters (e.g., bandpass filter) that model the bias or noise, and the filtered data may then be compared for consistency. In some examples, consistency may be based on residuals or some other metric describing discrepancy among the filtered sensor data.

Abstract: Techniques associated with generating and maintaining sparse geographic and map data. In some cases, the system may maintain a factor graph comprising a plurality of nodes. In some cases, the nodes may comprise pose data and sensor data associated with an autonomous vehicle at the geographic position represented by the node. The nodes may be linked based on shared trajectories and shared sensor data.

Abstract: A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. The sensor pod system may include a cleaning system to clean sensing surfaces of sensor pods during operation. The sensor pod system may include sensors of different types and modalities. Sensor pods of the sensor pod system may be modularly installed on a vehicle, for example, an autonomous vehicle and collect and provide data of the environment during operation of the vehicle.

Abstract: Techniques are discussed for modifying map elements associated with map data. Map data can include three-dimensional data (e.g., LIDAR data) representing an environment, while map elements can be associated with the map data to identify locations and semantic information associated with an environment, such as regions that correspond to driving lanes or crosswalks. A trajectory associated with the map data can be updated, such as when aligning one or more trajectories in response to a loop closure, updated calibration, etc. The transformation between a trajectory and an updated trajectory can be applied to map elements to warp the map elements so that they correspond to the updated map data, thereby providing automatic and accurate techniques for updating map elements associated with map data.

Abstract: A robot end effector for dispensing an extrudable substance comprises a chassis, cartridge bays, attached to the chassis and each shaped to receive a corresponding one of two-part cartridges, and a manifold, comprising a manifold outlet, manifold inlets, and a valve inlet. When the two-part cartridges are received by the cartridge bays, the manifold inlets are in fluidic communication with corresponding ones of the two-part cartridges via static mixers, attached to cartridge outlets of the two-part cartridges. The robot end effector additionally comprises a head assembly, comprising pairs of fittings, configured to selectively supply compressed air from a pressure source to the two-part cartridges when the two-part cartridges are received by the cartridge bays, so that contents of the two-part cartridges are concurrently extruded through the cartridge outlets.

Abstract: Generating a map associated with an environment may include collecting sensor data received from one or more vehicles and generating a set of links to align the sensor data. A mesh representation of the environment may be generated from the aligned sensor data. A system may determine a proposed link to add, a proposed link deletion, and/or a proposed link alteration, and receive a modification comprising instructions to add, delete, or modify a link. Responsive to receiving a modification, the system may re-align a window of sensor data associated with the modification. The modification and/or sensor data associated therewith may be collected as training data for a machine learning model, which may be trained to generate link modification proposals and/or determine sensor data that may be associated with a poor sensor data alignment.

Abstract: Techniques for, among other things, detecting faults associated with inertial measurement units (IMUs) of a vehicle when multiple IMUs are coupled to the vehicle are described herein. The techniques may include receiving first data from a first IMU of the vehicle and receiving second data from a second IMU of the vehicle. Based at least in part on the first data and the second data, a rotation of the first IMU relative to the second IMU may be calculated. The calculated rotation between the first IMU and the second IMU may be indicative of a fault associated with the first IMU or the second IMU. In response to detecting the fault, an action may be performed with respect to the first IMU or the second IMU to correct for the fault.

Abstract: Techniques associated with generating and maintaining sparse geographic and map data. In some cases, the system may maintain a factor graph comprising a plurality of nodes. In some cases, the nodes may comprise pose data and sensor data associated with an autonomous vehicle at the geographic position represented by the node. The nodes may be linked based on shared trajectories and shared sensor data.

Abstract: Motion can be induced at a vehicle, e.g., by actuating components of an active suspension system, and first sensor data and second sensor data representing an environment of the vehicle can be captured at a first position and a second position, respectively, resulting from the induced motion. A second sensor can determine motion information associated with the first position and the second position. Calibration information about the sensor, the first sensor data, and the motion information can be used to determine an expectation of sensor data at the second position. A calibration error can be the difference between the second sensor data and the expected sensor data.

Abstract: A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may have an effective field of view created by individual sensors with overlapping fields of view. The sensor pod system may include sensors of different types and modalities. Sensor pods of the sensor pod system may be modularly disposed on a vehicle, for example, an autonomous vehicle to collect and provide data of the environment during operation of the vehicle. The sensor pods may be disposed at elevated locations around the vehicle to reduce obstacles within the sensor pods fields of view.

Abstract: Techniques for a pose component that may determine a pose are described herein. A pose may refer to the inertial pose or position of a vehicle which may be updated in real-time or near real-time. For example, the techniques may include receiving a plurality of input signals at a pose component and monitoring the plurality of input signals. The pose component may determine, based on the monitoring of the plurality of input signals, a particular pose update algorithm of a plurality of pose update algorithms for determining the pose and determine, using the particular pose update algorithm, the pose based the plurality of input signals and IMU measurements associated with a primary IMU.