Systems and methods are described for monitoring detection of objects in a sensor. The system can generate an occupancy array based on scene data corresponding to a scene of a vehicle. The array can include points that represent a location within the environment of the vehicle and an occupancy value that indicates whether an object is detected at that location. The system can modify an occupancy value of at least one point in the array and identify at least one static object miss based on a group of occupancy values of a group of points in the array.

Systems and methods for image-based perception. The methods comprise: capturing images by a plurality of cameras with overlapping fields of view; generating, by a computing device, spatial feature maps indicating locations of features in the images; identifying, by the computing device, overlapping portions of the spatial feature maps; generating, by the computing device, at least one combined spatial feature map by combining the overlapping portions of the spatial feature maps together; and/or using, by the computing device, the at least one combined spatial feature map to define a predicted cuboid for at least one object in the images.

According to one aspect, a lidar system is a lidar system which includes one set of mechanical, e.g., optical, components, and two or more sets of electrical and/or software components. The beams which are provided by the optical components are effectively alternated between a first and second sets of electrical and/or software components. The redundancy provided by the first and second sets of electrical and/or software components allows the lidar system to remain operational in the event that one set of electrical and/or software components becomes non-operational.

Examples described herein provide a method that includes performing a first scan of an object to generate first scan data. The method further includes detecting a defect on a surface of the object by analyzing the first scan data to identify a region of interest containing the defect by comparing the first scan data to reference scan data. The method further includes performing a second scan of the region of interest containing the defect to generate second scan data, the second scan data being higher resolution scan data than the first scan data. The method further includes combining the first scan data and the second scan data to generate a point cloud of the object.

The offline map generation process may collect multiple point cloud data of the same area. A perception algorithm may operate on the point cloud data to detect static objects, which may be fixed road features that do not change among the point cloud data, allowing the perception algorithm to more accurately detect the static objects. During online operation of the ADV through the area, the ADV may trim regions-of-interest (ROI) of the area to exclude the predefined static objects. The perception algorithm may execute the sensor data of the ROI in real-time to detect objects in the ROI. The may be added back to the output of the perception algorithm to complete the perception output.

The offline map generation process may collect multiple point cloud data of the same area. A perception algorithm may operate on the point cloud data to detect static objects, which may be fixed road features that do not change among the point cloud data, allowing the perception algorithm to more accurately detect the static objects. During online operation of the ADV through the area, the ADV may trim regions-of-interest (ROI) of the area to exclude the predefined static objects. The perception algorithm may execute the sensor data of the ROI in real-time to detect objects in the ROI. The may be added back to the output of the perception algorithm to complete the perception output.

A time-of-flight light detection system includes: a plurality of circuits arranged sequentially along a signal path that comprises a plurality of signal channels, the plurality of circuits including a first circuit and a second circuit arranged downstream from the first circuit; a reference signal source configured to generate a plurality of reference signals, where each of the plurality of signal channels at the first circuit receives at least one of the plurality of reference signals; and an evaluation circuit coupled to the plurality of signal channels to receive a processed reference signal from the signal path, the evaluation circuit further configured to compare the processed reference signal to a first expected result to generate a first comparison result.

A time-of-flight light detection system includes: a plurality of circuits arranged sequentially along a signal path that comprises a plurality of signal channels, the plurality of circuits including a first circuit and a second circuit arranged downstream from the first circuit; a reference signal source configured to generate a plurality of reference signals, where each of the plurality of signal channels at the first circuit receives at least one of the plurality of reference signals; and an evaluation circuit coupled to the plurality of signal channels to receive a processed reference signal from the signal path, the evaluation circuit further configured to compare the processed reference signal to a first expected result to generate a first comparison result.

An automatic wall climbing type radar photoelectric robot system for damages of a bridge and tunnel structure, mainly including a control terminal, a wall climbing robot and a server. The wall climbing robot generates a reverse thrust by rotor systems, moves flexibly against the surface of a rough bridge and tunnel structure by adopting an omnidirectional wheel technology, and during inspection by the wall climbing robot, bridges and tunnels do not need to be closed, and the traffic is not affected. Bridges and tunnels can divide into different working regions only by arranging a plurality of UWB base stations, charging and data receiving devices on the bridge and tunnel structure by means of UWB localization, laser SLAM and IMU navigation technologies, a plurality of wall climbing robots supported to work at the same time, automatic path planning and automatic obstacle avoidance realized, and unattended regular automatic patrolling can be realized.

A method may include obtaining sensor data from one or more LiDAR units and determining a point-cloud corresponding to the sensor data obtained from each respective LiDAR unit. The method may include aggregating the point-clouds as an aggregated point-cloud and generating an initial proposal for a two-dimensional ground model made of multiple grid blocks. The method may include filtering out unrelated raw data points from each grid block of the plurality of grid blocks to generate a filtered point-cloud matrix. The method may include identifying one or more surface-points and one or more object-points included in the filtered point-cloud matrix and generating an array of extracted objects based on the object-points.