Localization for a car is when the position and pose of a car is contextualized in a shared co-ordinate system (map). It is important in autonomous driving because if a car is accurately localized in a map, it can get context on the relevant fixed infrastructure such as regulatory signs, relevant signal lights, and lane markings and understand their relationships to the car. It is important to localize the car in all six dimensions {x, y, z, roll, pitch, yaw}. Civil Maps projects the map data into the field of view of a real time sensor such as a camera or LiDAR to help the decision engine to correctly contextualize 3D features such as stop signs, signal lights, and lane markings.
Traditionally, other localization techniques utilize differential Global Positioning System (GPS) and Inertial Measurement Unit (IMU) to place a car in the environment. The GPS technique of localization relies on a time of the signal from multiple positioning satellites.
The error in GPS is substantial in urban environments where there are buildings that do not let the signal from the GPS satellites reach the GPS receiver directly, the error commonly referred to as multi path issue. The IMU starts drifting significantly if it does not get correction from an orthogonal approach such as GPS.
Other mapping providers mainly focus on lateral (x) and longitudinal (y) localization. Their maps are also in 2 dimensions and do not allow for 6 degrees of freedom. Civil Maps solves the challenge of real-time spatial transforms by compressing raw sensor data into signatures.
Signature Based Localization
At Civil Maps, we have developed a technique that utilizes signatures, reducing gigabytes of data into kilobytes of data. Using this approach we can then localize a car with six degrees of freedom using an off the shelf Arm Cortex processor in real time.
Map Projections
Once an autonomous car is locked on to its position and pose, it can use a priori information such as a map to annotate or place bounding boxes into the real sensor space. The ability to move back and forth between the geo-reference frame and the vehicle reference frame allows for the usage of a priori information to drastically reduce the computational overhead.