{"id":45280,"date":"2019-06-07T17:02:49","date_gmt":"2019-06-07T23:02:49","guid":{"rendered":"https:\/\/www.realsenseai.com\/uncategorized-cn\/depth-and-tracking-combined-get-started\/"},"modified":"2026-03-19T15:58:08","modified_gmt":"2026-03-19T21:58:08","slug":"depth-and-tracking-combined-get-started","status":"publish","type":"post","link":"https:\/\/www.realsenseai.com\/cn\/news-insights-cn\/depth-and-tracking-combined-get-started\/","title":{"rendered":"RealSense\u2122 tracking camera T265 and depth cameras D400 series \u2013 Better together"},"content":{"rendered":"\nWhile many people are familiar with RealSense\u2122 technology as a producer of high-quality depth cameras, the introduction of the RealSense\u2122 Tracking Camera T265 has created a lot of questions about the difference between our tracking and depth solutions, and the applications for both. With a long history of leadership in the depth camera space, has produced many different types of depth devices, currently featuring both Stereo<\/a> and Coded Light<\/a> solutions which are used in a wide variety of applications.\n\nThe depth and tracking cameras are sold separately and have many uses individually, however there are also a significant number of spaces where combining depth information with an accurate V-SLAM tracking solution<\/a> can be beneficial. In this post, we will explore a few of those use cases and why adding tracking to depth leads to a better-together result.\n

The difference between tracking and depth<\/strong><\/h3>\nIn some ways it is useful to consider tracking and stereo depth as different versions of the same problem space \u2013 given a set of known information, both systems use algorithms to determine unknown information. In the case of a stereo depth camera, the known information is the baseline between the two camera lenses. By comparing the image each camera lens sees, and looking at individual points of interest, the depth distance of each pixel in the space (and therefore the distance of each object) can be known essentially by drawing a triangle between the point in space and the two cameras and using trigonometry to infer the distance.\n\n\"\"\n\nTracking, on the other hand, is concerned not with knowing how far away a particular object is, but rather, with understanding the position and movement of the tracking camera. The T265 doesn\u2019t track external objects, its job is to track itself with a high degree of accuracy. It does that by using its own set of known information. It has two cameras, which have their own known baseline. It also contains an inertial measurement unit (or IMU), which is a combination of gyroscopes and accelerometers similar to that contained in most modern cellphones. The cameras search for visually distinct features in the environment, compare them between the two cameras and then combine that information with the motion of the device from the IMU to enable highly accurate and low latency position data.\n

Robotics<\/strong><\/h3>\nFor robotics, one of the key problems is knowing where the robot is, and also being able to adapt to a changing environment, as well as being able to understand the whole of the environment it is in. A security robot that ends up in an ornamental fountain<\/a> is no longer performing its safety functions after all. A robot equipped with the T265 tracking camera can integrate wheel odometry data for a very accurate understanding of its position within the environment, but would struggle to avoid the pond without virtual fencing, and would not be able to avoid a person walking in front of the robot. By adding a D435 or D415 depth camera, the robot can understand obstacles in real-time with no need to pre-map the space.\n\n\"\"\n\nFor example, if the security robot in our example has identified something it needs to navigate towards \u2013 an object of interest, the data from the T265 can be used to plot a path towards this object of interest. Even if obstacles are placed in the way of the robot, the D435 or D415 can identify the obstacle, allowing the robot to navigate around it. In the video below, we throw obstacles in the path of the robot. Even though these are random, the robot can accurately assess and move around it before returning to a path that will take it to target. Combining the two devices together gives a robot a broader understanding of the space that it is in, and allow it to create an occupancy map of the environment and navigate through it with ease. The depth camera (providing it is oriented correctly) could even identify the downward slope or steps towards the fountain and avoid those hazards too.\n\n