A navigation apparatus includes an image capturing device, template database, correlation device, evaluation device, and output interface. The image capturing device can create a radar image of a surround, the template database configured to provide at least one template substantially matched to the radar image and containing at least one geo-referenced landmark, the at least one geo-referenced landmark being geo-referenced by at least one geo-coordinate. The correlation device can correlate the at least one geo-referenced landmark in the at least one template with the radar image and provide the at least one geo-coordinate belonging to the at least one geo-referenced landmark. The evaluation device can determine a position of the navigation apparatus from the at least one geo-coordinate of the at least one geo-referenced landmark and from a setting of the image capturing device. The output interface is configured to provide the determined position.

Systems and methods of processing crowdsourced navigation information for use in autonomous vehicle navigation are disclosed. A method may include processing, by a mapping server, crowdsourced navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.

A location-estimating device comprises a processor configured to acquire first locational information representing the location of a moving object for each first information acquisition time and calculate a first estimated location, acquire second locational information representing the location of the moving object for each second information acquisition time and calculate a second estimated location, and when the first estimated location has been determined, calculate the first movement amount and moving direction between the first information acquisition time and current time, and estimate the location at the current time based on the first estimated location, first movement amount and moving direction, or when the first estimated location is not determined, calculate a second movement amount and moving direction between the preceding second information acquisition time and current time, and estimate the location at the current time based on the second estimated location, second movement amount and moving direction.

A method for locating an object of interest using offset. The object may be a mobile platform, or portion of same, associated with a vehicle, or a pavement segment or feature of or on a pavement segment on which the mobile platform is located. The vehicle includes first and second fixed points having a known offset from each other. An image sensor whose field of view includes the second fixed point and a portion of the mobile platform provides image data which is used with the known offset to calculate the precise location of the object of interest.

A multi-sensor fusion SLAM system and a robot. The system operates on a mobile robot and comprises: a visual inertia module, a laser scanning matching module, a loop closure detection module, and a visual laser image optimization module. According to the multi-sensor fusion SLAM system and the robot, the calculation amount of laser matching constraint optimization can be reduced by using a voxel subgraph so that the pose calculation is more accurate, accumulated errors of long-time operation of the system can be corrected in time by means of sufficient fusion of modules, and the robustness of the system and the accuracy of positioning and mapping are integrally improved.

A navigation system for a host vehicle may include a processor programmed to: receive from a center camera onboard the host vehicle a captured center image including a representation of at least a portion of an environment of the host vehicle, receive from a left surround camera onboard the host vehicle a captured left surround image including a representation of at least a portion of the environment of the host vehicle, and receive from a right surround camera onboard the host vehicle a captured right surround image including a representation of at least a portion of the environment of the host vehicle; provide the center image, the left surround image, and the right surround image to an analysis module configured to generate an output relative to the at least one captured center image; and cause a navigational action by the host vehicle based on the generated output.

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).

The disclosure belongs to the technical field of integrated navigation under non-ideal conditions, and in particular relates to a robust filtering method for integrated navigation based on a statistical similarity measure (SSM). In view of the situation that there are normal beam measurement information of the DVL and beam measurement information with a large error simultaneously in a SINS/DVL tightly integrated system, and aiming at the problem that the existing robust filters of an integrated navigation system process the measurement information in a rough manner and are likely to lead to loss of normal measurement information, the disclosure proposes a novel robust filtering method based on decomposition of multi-dimensional measurement equations and the SSM. The disclosure introduces the SSM theory while decomposing the multi-dimensional measurement equations of the SINS/DVL tightly integrated navigation system, and assists the measurement noise variance of each beam to complete respective adaptive update in case of a large measurement error, finally ensuring independence of processing of the measurement information of each beam. The disclosure can be used in the field of integrated navigation of underwater vehicles under non-ideal conditions.

A robotic system is disclosed that uses autonomous tunnel navigation. The system includes a plurality of sensors (e.g., ranging, odometry) to measure a distance from the robotic system to a plurality of walls. Memory stores instructions and a processor is coupled to the memory and the plurality of sensors to execute the instructions. The instructions cause the robotic system to detect movement of the robotic system through a surrounding environment based on sensor measurements, determine if the robotic system is in a tunnel based on the sensor measurements, and navigate with the odometry-based sensor when the robotic system is determined to be in the tunnel or the ranging sensor when the robotic system is determined to be not in the tunnel.

To provide an own position estimation apparatus and an own position estimation method which can correct the position coordinate of own vehicle, even if a periphery monitoring apparatus in which detection points detected with good accuracy at the same timing is few is used. An own position estimation apparatus detects relative positions of a road side wall based on detection information of a periphery monitoring apparatus; converts the past relative positions, into relative positions on a basis of the current position of the own vehicle, and superimposes the current relative positions and the past relative positions after conversion; searches for a relative position relation that a coincidence degree between the relative positions of the road side wall after superposition and the positions of the road side wall of the map data becomes high; and corrects the position coordinate of the own vehicle based on the relative position relation.