When automobiles were first introduced, they were used only for transportation—to quickly get from point A to point B. While this was revolutionary and changed the landscape as humans know it, there were more innovations to come. Automobiles have become smarter as more intelligence has been added, starting first with driver assistance applications, such as anti-lock brakes and power steering. Now, with artificial intelligence (AI), self-driving vehicles are on the horizon. One of the key ingredients to autonomous vehicles (AVs) is the ability to track the location and movement of the vehicle.
With the introduction of consumer automotive GPS technology in the ’90s, tracking movement became a relatively easy task. This technology opened the door to several navigation- and route-planning applications. GPS does have its limitations, however. It is accurate only to within a few meters, thereby restricting its use to applications in which tracking small or “micro-movements” is not necessary. And, in certain areas where access to GPS satellites is limited (cities with tall buildings, mountains, etc.), you don’t have access to the data that GPS supplies, nullifying its use. As vehicles are becoming more autonomous and “aware” of their surroundings, tracking these micro-movements is now becoming necessary; therefore, we must look beyond what GPS offers.
Fortunately, simultaneous localization and mapping (SLAM) can orient you to within inches and doesn’t require satellite connectivity. SLAM is the computational problem of constructing a map in an unknown environment while simultaneously keeping track of your position (location and orientation) within that environment. SLAM comprises tracking six degrees of freedom (6DoF), which is composed of three degrees for position (up/down, back/forward, and right/left), and three for orientation (yaw, pitch, and roll) to understand your position in an environment (Figure 1).
SLAM has extensive usages; for example, consider a mapping application. SLAM can be used to identify where you are facing in an environment—for example, facing northwest at an intersection—then the application can tell you whether to turn right or left. A simple GPS calculation only tells you that you are at an intersection; the application won’t know which way you are facing until you have already walked in the wrong direction.
SLAM is quickly becoming an important advancement in embedded vision as it enables a device with the ability of location awareness. Using SLAM, a vehicle can not only track where it is heading or its direction (orientation), but also how it is moving within its surrounding environment (location, speed and altitude).
Computations for SLAM were typically performed with a camera sensor as the only form of input. This was known as Visual SLAM (VSLAM). But in the past few years, with the suite of additional sensors becoming available, SLAM has evolved to fusing additional sensor inputs.
A SLAM system works by tracking a set of points through successive camera frames and other sensor data to triangulate the camera’s 3D position, while simultaneously using this information to approximate camera (or another sensor) orientation. As long as there is a sufficient number of points being tracked through each frame, both the orientation of the sensor(s) and the structure of the surrounding physical environment can be rapidly understood.
So, for example, in the case of automotive implementations, SLAM may use a combination of one or more forward-facing cameras, radar, lidar, and inertial measurement units (IMUs, which provide data from accelerometers and gyroscopes that help to estimate the sensor’s orientation) as inputs. SLAM is then used to determine how the vehicle is moving in the environment. When GPS data is available, it can be used to fortify the position estimate.
Figure 2 shows an example in which a variety of sensors, such as camera, lidar, and radar, is mounted around the vehicle that can be used as input for SLAM.
SLAM is a key ingredient in many applications that are used for driver assistance and self-driving vehicles. A few of these applications include:
As shown in Figure 3, the market size for SLAM-based applications is set to exceed $2 billion by 2024 [1]. Major drivers for this market growth are the advancements in SLAM algorithms and the growth of SLAM in various markets. The rising technological developments and growing awareness regarding the benefits offered by SLAM are primarily driving the market demand. Growing interest in the technology, particularly from industries including autonomous vehicles and augmented virtual reality, has resulted in the adoption and expansion of SLAM across the globe.
Moreover, SLAM used for navigation in both indoor and outdoor environment applications opens an opportunity for the larger adoption of the technology across various end-user industries. Over the past five years, leading technology companies have made significant investments in SLAM to integrate into various business expansion strategies such as new product developments and mergers and acquisitions.
Figure 4 shows a generalized flow of SLAM.
Each of the blocks is based on a classical computer vision (CV) approach. However, they rely heavily on a variety of linear algebra and matrix operations, so they are computationally heavy, and can be implemented on a CPU or GPU.
Using a CPU is great for general-purpose usage and prototyping, but it has limited performance capabilities. One limitation is a small number of SIMD lanes for parallel processing. Secondly, it is not power efficient, so it’s not the best option to scale, and, in some cases, may not be able to deliver SLAM performance in real time.
Using a GPU is the next level up, in terms of computational ability. It has a variety of modalities for parallel processing, which can help achieve great performance and to meet real-time requirements. But again, GPUs are also power-hungry and generate a lot of heat. Additionally, SoC vendors cannot justify adding the real estate needed for a GPU in their floorplan just to do processing in this way.
This is where a specialized DSP comes in. DSPs are highly programmable and require a small area, making them scalable for mass deployment in devices of various markets.
The Cadence® Tensilica® Q7 DSP is designed from the ground up to enable high-performance SLAM on the edge and in other devices. The Vision Q7 DSP is the sixth generation of vision and AI DSPs from the Tensilica family. Cadence has optimized instructions for faster performance on matrix operations, feature extraction, and convolutions to give the best performance yet on vision DSPs, providing the perfect balance of high performance and low power that is essential to SLAM applications at the edge. It can deliver up to 2X greater performance for vision and AI in the same area compared to its predecessor, the Tensilica Vision Q6 DSP.
Figure 5 shows the architecture and key features of this DSP.
The Tensilica Vision Q7 DSP offers the following high-level features:
Additionally, the Vision Q7 DSP is designed to meet ISO 26262 certification, making it a great platform for automotive applications. Below is a typical architectural diagram showing a variety of sensors connecting to the Vision Q7 DSP for the purposes of computing SLAM.
Additionally, the Vision Q7 DSP can also enable many decentralized and distributed systems, whereby the DSP can be placed near the sensors themselves and processes the data before it arrives at the CPU, reducing the memory bandwidth and data that needs to be transmitted. This approach is most commonly used in a complicated system like a vehicle to meet the needs of safety-critical and high-performance next-generation applications.
In addition to being fully supported in the Tensilica Xtensa® Xplorer development environment, the Vision Q7 DSP also leverages the mature and highly optimized Cadence Xtensa Imaging Library. Inspired by OpenCV (the C++ computer vision library), Cadence has ported many of the OpenCV functions, maintaining similar function names and API, so transitioning from OpenCV is straightforward.
The Vision Q7 DSP is supported by the Tensilica Neural Network compiler. The Tensilica Neural Network compiler maps neural networks into executable and highly optimized high-performance code for the Vision Q7 DSP, leveraging a comprehensive set of optimized neural network library functions.
Cadence has performed an in-house implementation of VSLAM using a single camera input and profiled the various blocks of the SLAM pipeline on both the Vision Q7 DSP and its predecessor, the Vision Q6 DSP (see Figure 7).
Figure 7: The Vision Q7 DSP speed over the Vision Q6 DSP: Up to 2X improvement on various blocks of SLAM
The Vision Q7 DSP shows close to 2X performance gain over the Vision Q6 DSP in various blocks of the SLAM pipeline. Improved instructions, optimized packages, and more MACs result in higher frequency for estimating camera position, and, furthermore, a better experience when the Vision Q7 DSP is used to accelerate SLAM-based applications. While providing this performance gain, the Vision Q7 DSP also requires the same area as the Vision Q6 DSP and consumes less power, making it the ideal offering for future products.
In this article, we introduced the concept of SLAM and walked through the implementation of our Vision DSPs targeting automotive usages. We have also shown a comparison between the Vision Q7 DSP and its predecessor, the Vision Q6 DSP, and the improvements in performance in the various blocks.
This article focuses on purely computer vision approaches to implement a SLAM workflow. Recent advances have been made by integrating various convolutional neural network (CNN) layers to enhance the key point matching and feature extraction stage amongst other building blocks. The Cadence Tensilica Q7 DSP supports many layers required by the latest neural networks, making this type of fusion between vision and AI possible on the same DSP.
This type of harmonious marriage between vision processing and AI is key to bring forth the next generation of SLAM-based applications to the automotive market.
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