1 Introduction

Whether it is a simple task such as lane assist or blind spot detection, or a more complex task such as autonomous navigation, understanding the surroundings of a vehicle or robot is vital for success, and thereby safety. Vehicles and robots perceive their environment by converting data captured by sensors such as, RADARs, LiDARs and Cameras in to a format that can be consumed by the vehicle’s decision-making engine. Light Detection and Ranging (LiDAR)-based maps tend to be the most accurate, however, they are typically cost prohibitive for most vehicles or robots. Therefore, RADAR and camera-based solutions tend to be more widely used.

The SFM algorithm is one of the more widely used algorithms for camera-based mapping. The SFM algorithm by itself outputs a point-cloud (a set of points extracted from surrounding objects), which can then be consumed by some type of mapping algorithm. The application described in this article feeds the point-cloud to an Occupancy Grid mapping algorithm to generate a map of the surroundings.

In automotive and robotics applications, the steps of receiving sensor data, converting the data to a usable format, and prescribing actions based on the perceived environment, are typically performed on an embedded platform. The Jacinto 7 TDA4x family of high-performance SoCs by Texas Instruments are designed from the ground up particularly to address the varied algorithmic needs of the automotive, industrial and robotics markets. The Structure From Motion, or SFM algorithm is one such algorithm around which the device was designed. As a result the key computational blocks of the algorithm map seamlessly to either hardware accelerators or general purpose processing cores on the TDA4VM device. This article describes the SFM based OG mapping algorithm, the TDA4VM device, and how the algorithm maps to the device to enable a high-fidelity real-time map of the environment, before showing some example implementations on the device with corresponding outputs.