2.1. Plane detection

Plane detection has been widely studied in recent decades and has been applied in many computer vision fields, such as occlusion detection [Reference Bagautdinov, Fleuret and Fua11] and human pose estimation [Reference Chen, Guo, Li, Lee and Chirikjian12]. Typically, we can find that Hough Transform, Random Sample Consensus (RANSAC) [Reference Fischler and Bolles13], and region-growing-based methods are usually used for plane detection.

For depth images, Vera et al. [Reference Vera, Lucio, Fernandes and Velho14] proposed a real-time technique for plane detection with an efficient Hough transform voting scheme, which has asymptotic time complexity of $\mathcal{O}(N)$ . Quadtree was used to identify clusters. Instead, Taguchi et al. [Reference Taguchi, Jian, Ramalingam and Feng10] used multi-stage RANSAC to extract plane primitives from the 3D point cloud, which is more flexible but has higher time complexity when too many outliers exist. Xiao et al. [Reference Xiao, Zhang, Zhang, Zhang and Hildre15] proposed a grid-based region-growing algorithm, as they noticed that if a point cloud was divided into small grids in structured environments, most of them were in a planar shape. Feng et al. [Reference Feng, Taguchi and Kamat16] constructed a graph for depth image and performed an agglomerative hierarchical clustering algorithm to merge nodes, which has been shown effective in CPA-SLAM [Reference Ma, Kerl, Stückler and Cremers8].

For stereo images, Piazzi et al. [Reference Piazzi and Prattichizzo17] showed that three corresponding noncollinear points are enough to compute the normal vector when the two cameras are aligned to the same orientation. They used Delaunay triangulation to group feature points and then detected coplanar triangles using a seed-growing strategy. Similar idea was adopted in ref. [Reference Aires, Araújo and Medeiros18], where Aires et al. used affine homography to represent a plane and compute reprojection errors to validate a plane.

2.2. Plane parameterization

There are lots of ways for plane parameterization. The Hesse notation is one of the most general models that represents a plane [Reference Weingarten, Gruener and Siegwart19], using normal $\textbf{n}$ and distance $d$ . However, the Hesse model is over-parametrized, which may lead to a rank-deficient information matrix, causing a Gauss-Newton solver to fail [Reference Kaess20]. An alternative solution is using Spherical coordinates, that is, $(\alpha, \beta, d)$ , as a minimal representation, but there are singularities that will also disturb optimization. An intermediate parameterization is to divide normal by distance [Reference Proenca and Gao21], which removes one degree of freedom (DoF). If distance is always far from zero, it is safe to update in the optimization.

Kaess proposed to represent a plane by using unit quaternions [Reference Kaess20], which is a minimal representation for plane without any singularities. The only problem is that it is not intuitive, that is, the geometric meaning is not clear. In ref. [Reference Geneva, Eckenhoff, Yang and Huang22], Geneva et al. used the Closest Point to represent a plane and also formulated a simple additive error model when updating the parameters during optimization. It is also singularity free when the value of distance does not equal to zero.

2.3. Optimization with planes

Similar to the case of point-to-point correspondences, a naive idea for planar SLAM is to minimize the distances between the associated plane pairs, which can be solved by decoupling the rotation and translation components [Reference Taguchi, Jian, Ramalingam and Feng10]. However, the direct model suffers from depth noise and inaccurate plane fitting. Another more common strategy is to minimize the distance between the point and its corresponding plane [Reference Ma, Kerl, Stückler and Cremers8, Reference Hsiao, Westman, Zhang and Kaess9]. CPA-SLAM [Reference Ma, Kerl, Stückler and Cremers8] used points and a global plane model to optimize the system, which aimed to reduce both photometric residuals and geometric residuals. The geometric residual came from the point-plane distances mentioned above and the point-point distances.

Instead of using range sensors, Pop-up SLAM [Reference Yang, Song, Kaess and Scherer23] is a monocular point-plane odometry, which extracts semantic planar information through CNN ground segmentation and gets normal vectors from it. The planar information was used to enhance depth and implement a planar alignment. However, Pop-up SLAM uses quaternion to represent planes, which does not have a clear geometric meaning.

3.1. Notation

We denote a 3D point by $\textbf{p}$ and its corresponding pixel by $\textbf{u}$ . The projection between them is $\textbf{u}=\Pi (\textbf{p})$ , thus $\textbf{p}=\Pi ^{-1} (\textbf{u}, \rho )$ representing the reprojection process while $\rho$ is the inverse depth. $\textbf{K}$ represents the intrinsic matrix of the camera. The plane parameterization is denoted as $\textbf{n}$ , a minimal representation of plane described in Section 4. $\Omega \in \textbf{R}^2$ represents a 2D continuous domain, and $\Omega _{\textbf{n}}$ is considered as a planar region.

Camera pose is denoted by transformation matrix $\textbf{T} \in SE (3)$ , that is, $\textbf{T}_{cw}$ transforms a point from the world coordinate to the camera coordinate, and $\textbf{T}_{wc}$ the opposite. The pose update is represented by Lie-algebra elements $\boldsymbol\xi \in \mathfrak{se} (3)$ with a left-multiplicative formulation. Similar to most papers, bold lower-case letters represent vectors while bold upper-case letters represent matrices in this paper.

3.2. DSO

As our work is mainly based on DSO, it is necessary to briefly introduce the framework of DSO at first. DSO optimizes photometric errors in a sliding window [Reference Engel, Koltun and Cremers6], and the energy function for a single residual is defined as:

(1) \begin{equation} E_{\textbf{u}j}\,{:\!=}\,\sum _{\textbf{u}\in \mathcal{N}(\textbf{u})}w_{\textbf{u}}\left\|(I_j[\textbf{u}']-b_j)-\frac{t_je^{a_j}}{t_ie^{a_i}}(I_i[\textbf{u}]-b_i)\right\|_{\gamma }, \end{equation}

(2) \begin{align} \textbf{u}'=\Pi (\textbf{T}\Pi ^{-1} (\textbf{u}, \rho )),\\[-7pt] \nonumber\end{align}

where $I$ denotes an image, $t$ is the exposure time, $a,b$ are the affine brightness parameters, $w$ means the weight of each point, $\|\cdot \|_{\gamma }$ represents the Huber norm, and $\mathcal{N}(\textbf{u})$ is the set of neighbors of pixel $\textbf{u}$ with a specific pattern. Eq. (1) defines the cost of an observation that point $\textbf{p}$ in the $j$ th keyframe projected into the $i$ th keyframe.

The optimization is implemented whenever a new keyframe is inserted into the sliding window, using a Gaussian-Newton method. After optimization, an additional Hessian Matrix is processed to keep all constraints from marginalized frames and points. For other frames, they are used to help all immature points to converge, using a simple idepth filter.

4.1. Limitation of point

DSO extracts point candidates in the regions with enough gradient and regards one point as the intersection of the source pixel’s ray and the object. So only one parameter, that is, inverse depth, is used to represent the point. In the optimization process, residual pattern is used to enhance the differentiation of a single pixel, considering the trade off between computation and providing sufficient information. Several nearby pixels share the inverse depth of the middle pixel, which implies the fronto-parallel assumption that the surface of object is assumed to be parallel to the camera. Obviously, this assumption is just an approximation in the real world, which may fail to hold slanted surfaces, as Fig. 1 shows.

Figure 1.

Approxiamtation error using a residual pattern in different situations. We can find that there is a certain deviation between the actual depth (bold red line) and the approximation (bold black line). The two triangles represent the camera poses at different times.

As the inverse depth only tells us where the point is, information about the orientation is not included. Therefore, the approximation error is unenviable in a point-based odometry. Instead, if the normal vector of the surface is known, the deviation can be reduced to zero theoretically. By providing useful information about the normal vectors of all detected planes, the final energy function is much easier to converge, especially in structured environments where large numbers of planar patches can be found.

4.2. Plane parameterization

The most intuitive representation of plane is Hesse notation, that is,

(3) \begin{equation} \textbf{n}_0^T \textbf{p}-d=0, \end{equation}

where $\textbf{n}_0$ represents the unit normal vector of the plane and $d$ is the distance from the camera to the plane.

The parameterization $[\textbf{n}_0,d]$ is not a minimal representation, which has four variables while the DoF of plane is just three. Similar to [Reference Kaess20], we remove one DoF by forcing the distance term to 1:

(4) \begin{equation} \textbf{n}^T \textbf{p} -1=0, \end{equation}

where the plane parameterization $\textbf{n}=\textbf{n}_0/d$ becomes a minimal representation. Note that when $d=0$ , there is also singularity. To avoid this, we will not build a global planar model similar to ref. [Reference Ma, Kerl, Stückler and Cremers8]. Instead, we only represent a plane in the camera coordinate. A plane with $d$ approximating zero in the camera coordinates indicates that the plane passes through the camera center, which means it will be projected as a line (i.e., not detected as a plane). The plane will not be observed, thus avoiding from being added into the final optimization process. Local planar patches may not provide long-time consistent constraints as those of global planar models, but have much higher flexibility and adaptability.

The geometric meaning of this parameterization is also clear: the norm of $\textbf{n}$ is the inverse distance from the origin point to the plane, while its direction represents the normal. So $\textbf{n}$ is also called as inverse normal in this paper, which is illustrated in Fig. 2.

Figure 2.

Illustration of inverse normal.

From the pinhole imaging model,

(5) \begin{equation} \textbf{p}=\rho ^{-1}\textbf{K}^{-1}\textbf{u}, \end{equation}

and Eq. (4), we can get the relation between inverse depth and inverse normal:

(6) \begin{equation} \rho = (\textbf{K}^{-1}\textbf{u})^T\textbf{n}, \end{equation}

We use $\textbf{p}=\Lambda (\textbf{u},\textbf{n})$ to represent this transformation.

The partial derivative of the inverse depth about the inverse normal can also be derived easily:

(7) \begin{equation} \frac{\partial \rho }{\partial \textbf{n}}=(\textbf{K}^{-1}\textbf{u})^T. \end{equation}

Then, we can get the Jacobin matrix about inverse normal through the chain rule $\frac{\partial f}{\partial \textbf{n}}=\frac{\partial f}{\partial \rho }\frac{\partial \rho }{\partial \textbf{n}}$ directly, based on the existing Jacobin matrix from DSO. Here, $f$ is the residual of all observations.

4.3. Model

We formulate the whole optimization problem with planes in this section, which is illustrated in Fig. 3. Compared to DSO, our point-plane system add additional planar constraints into the optimization.

Figure 3.

Illustration of inverse normal.

Suppose that we have got some planar patches in the local window with a set of keyframes $\mathcal{F}$ , the full target function is given by

(8) \begin{equation} E\,{:\!=}\,\sum _{i\in \mathcal{F}}\left(\sum _{\textbf{u} \in \mathcal{P}_i}\sum _{j\in obs(\textbf{u})}E_{\textbf{u}j}+\sum _{\textbf{n} \in \mathcal{N} _i}\sum _{j\in obs(\textbf{n})}E_{\textbf{n}j}\right), \end{equation}

where $\mathcal{P}_i$ represents all points in the $i$ th keyframe except those in the planar regions, $\mathcal{N}_i$ is the set of all available planes, $obs(\textbf{x})$ returns all observations of target $\textbf{x}$ over all frames in the window, and $E_{\textbf{n}j}$ defines the cost energy of a planar patch $\textbf{n}$ observed from another view $j$ , that is,

(9) \begin{equation} E_{\textbf{n}j}\,{:\!=}\,\sum _{\textbf{u}\in \Omega _{\textbf{n}}}w_{\textbf{u}}\left\|(I_j[\textbf{u}']-b_j)-\frac{t_je^{a_j}}{t_ie^{a_i}}(I_i[\textbf{u}]-b_i)\right\|_{\gamma }, \end{equation}

(10) \begin{align} \textbf{u}'=\Pi (\textbf{T}\Lambda (\textbf{u}, \textbf{n})), \end{align}

where $\Omega _{\textbf{n}}$ is a region regarded as a plane, the extraction of which is discussed in next sections.

From Eqs. (8)--(9), we can find that only photometric residuals are used in our model. Geometric residuals are not adopted here. This is because that as we extract planar information from a monocular sparse odometry, the contours of planes are not clear; that is, it is difficult to judge if a projection pixel belongs to the plane. What’s more, the normal vectors are not as reliable as that from dense sensors. Inaccurate geometric constraints may destroy the stability of the system. Instead, photometric errors accumulated from single points are more flexible. A sudden increase of errors will be considered as a outlier or in bad conditions like being obscured and then be omitted in this time.

Another advantage is that no planar data association is needed in this way. Planar features are not distinguished as those of points. Some works like refs. [Reference Salas-Moreno, Glocken, Kelly and Davison7] and [Reference Hsiao, Westman, Zhang and Kaess9] used exhaustive searches over all candidates using intuitive metrics like the differences of normal vectors and distances, overlaps, and projection errors. However, these intuitive metrics are not appropriate for our small planar patches. With a direct photometric optimization, we avoid from finding tedious data association among planar patches.

The factor graph of the whole model is shown in Fig. 4. We can find that with the help of inverse normal, large numbers of variables about inverse depth can be reduced to only one variable, inverse normal, thus simplifying the graph. For example, without $\textbf{n}_1$ , there are hundreds of inverse depth $\rho$ that should be added into the factor graph. The simplification utilizes the important prior information, that is, points in a same plane shares common parameters. Therefore, the number of final variables to be optimized is significantly less than that of DSO.

Figure 4.

Factor graph of the point-plane odometry model. Here we show three keyframes with three points and one plane. Each factor term depends on the host frame (blue), target frame (red), and the point’s inverse depth or the plane’s inverse normal (black). The lines about planes are all bold.

4.4. Optimization

The Gauss-Newton method is applied to optimize all variables in the sliding window. The variable $\textbf{x}$ to be optimized consists of the increments of the camera intrinsic parameters $\textbf{c}$ , camera poses $\textbf{T}$ , light affine parameters $a,b$ , inverse depth $\rho$ of all active points, and inverse normals $\textbf{n}$ of all active planes. Suppose that there are $k$ frames, $l$ points, and $m$ planes in the current window. Then the state variable

(11) \begin{equation} \textbf{x}=\bigg[\underbrace{\delta \textbf{c}^T, (\boldsymbol\xi _1^T,\delta a_1,\delta b_1),\cdots,(\boldsymbol\xi _k^T,\delta a_k,\delta b_k)}_{\textbf{x}_\alpha }, \underbrace{\delta \rho _1,\cdots,\delta \rho _l,\delta \textbf{n}_1^T,\cdots,\delta \textbf{n}_m^T}_{\textbf{x}_\beta }\bigg]^T, \end{equation}

can be computed through the linear equation

(12) \begin{equation} \textbf{J}^T\textbf{W} \textbf{J}\textbf{x}=-\textbf{J}^T\textbf{W}\textbf{r}, \end{equation}

where $\textbf{J}$ is the Jacobin matrix of the photometric residual $\textbf{r}$ about the variable $\textbf{x}$ , and $\textbf{W}$ is the weight matrix set intuitively. By using the Schur complement, the part $\textbf{x}_{\alpha }$ can be derived firstly and then be used to compute $\delta \rho$ and $\delta \textbf{n}$ , respectively. Though the dimension of $\textbf{x}_\beta$ is much larger than that of $\textbf{x}_\alpha$ , the computation is fast due to the sparsity of the Hessian matrix. What’s more, the adoption of inverse normal further reduces the dimension of $\textbf{x}_\beta$ . One important thing to note is that each plane patch must include enough active points to avoid from rank-deficient information matrix.

The marginalization of planes follows the strategy of that of points. One difference is that the planes stay longer, as the projections of planar residuals cover a much larger area.

6.1. Quantitative comparison

Figure 7(a) illustrates the alignment errors for both DSO and SPPO on the full TUM monoVO dataset. Generally speaking, the error distributions of the two are similar, while SPPO seems a little more robust on some sequences. This is clearer in Fig. 7(b), where we show the median values of each runs. From Fig. 7(b) we can find that in some scenes like Sequence 36, our SPPO performs much better. This is due to the fact that our approach utilizes more priori knowledge, that is, planar constraints in a small region, to help the optimization of the system. However, we can also find that in some sequences our approach does not even perform as well as DSO, which is due to the fact that natural environments are also included in some sequences.

Figure 7.

Results of the full TUM monoVO dataset. (a) Colorbar representation of all runs. Each sequence is implemented 10 times for both DSO and SPPO, which corresponds to the horizontal axis. And the vertical axis shows the index of sequences. (b) Median values of 10 runs for each run. The mean error of DSO is 1.274 while for SPPO it is 1.222.

Due to proper planar constraints, our SPPO runs faster than DSO, which is shown in Fig. 8. The mean running time of DSO is 33.2 ms while for SPPO, it is 25.3 ms. What’s more, in most sequences our approach is about 20% faster than DSO. The improvement is pretty important for some hardware-limited applications like mobile robots. The high efficiency mainly comes from three factors: (1) our efficient plane detection algorithm gives almost no pressure on the computing sources; (2) the introduction of planar constraints helps to reduce the size of the Hessian Matrix in the optimization, avoiding from unnecessary updates of points; (3) the planar constraints accelerates the convergence of the optimization.

Figure 8.

Running time of each sequences. We adopt the median value of 5 runs to avoid from stochastic factors. Here we do not force real-time for both two methods, and multi-threads are also opened. And the size of all rectified images is 640x480.

6.2. Discuss

Plane detection is a key step in the proposed point-plane odometry. Our grid-based plane detection algorithm performs well in most scenes, except some natural environments like lawn. The TUM monoVO dataset provides some outdoor scenes including lawn, forest, and so on. Fig. 9 shows four samples of plane detection in different scenes. In Fig. 9(a), two virtual planes are detected due to regular edge protrusions. And in Fig. 9(b), no planes are detected in a natural forest, which implies SPPO degenerates into DSO in fact. Fig. 9(c) shows some detected planes on the lawn while Fig. 9(d) illustrates a proper plane detected on a wall. In both Fig. 9(a) and (c), the detected planes are not stable, which implies that the additional planar constraints may give false optimization directions. This explains why our SPPO may perform a little worse than DSO in some sequences (see Fig. 7). Therefore, our SPPO is suggested to run in a structured environment, especially indoor scenes.

Figure 9.

Four samples of plane detection. (a) A virtual plane; (b) No planes are detected in a natural forest; (c) False detections on the lawn; (d) A proper detection in structured environments.

When detecting planes, one important consideration is the factor between two eigen values, that is, $k_1,k_2$ in Eq. (12). The value of $k_1$ is used to find proper planes while the value of $k_2$ is used to avoid from bad planes (clustered in a line). Figure 10 illustrates the accumulative alignment errors with different $k_1$ , from 100 to 10,000. We can find that the trends at the earlier stage are close to each other, which means they all work well in “good” environments. However, if $k_1=10,000$ , which implies less planes are added into the optimization, the errors grow much faster. Therefore, our point-plane joint optimization approach really helps to control the error growth in some situations.

Figure 10.

Accumulative alignment errors with different settings.